Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Sexual function and pelvic floor function in men with systemic sclerosis compared to healthy controls: a cross-sectional study

OBJECTIVES

This cross-sectional study aimed to compare the sexual function (SF) and pelvic floor function of men with systemic sclerosis (SSc) with age-matched healthy controls (HC) and to identify the implications of clinical features on SF.

MATERIAL AND METHOD

Twenty SSc males and 20 HC aged 18-70 years completed eleven questionnaires assessing SF [International Index of Erectile Function (IIEF), Male Sexual Health Questionnaire (MSHQ)]; sexual quality of life: Sexual Quality of Life Questionnaire-Male (SQoL-M); pelvic floor function: Pelvic Floor Impact Questionnaire-Short Form 7 (PFIQ-7), fatigue, depression, physical fitness, functional disability, and quality of life. Clinical data were collected.

RESULTS

Significantly worse SF was observed in patients (median IIEF erectile function 12 in SSc versus 29 in HC, p < 0.001), with 70% reporting erectile dysfunction (ED) compared to 15% in HC. However, no significant difference was observed regarding pelvic floor function (median PFIQ7 8.8 in SSc versus 7.0 in HC, p = 0.141). Impaired SF was associated with higher disease activity, increased systemic inflammation, more pronounced fatigue, reduced physical fitness, severe depression, impaired overall quality of life, dyspepsia, and arthralgias (p < 0.05 for all).

CONCLUSIONS

Sexual dysfunction is highly prevalent in our SSc patients, whereas pelvic floor dysfunction is unlikely to be associated with these problems.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy

In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.

Functional connectivity changes in meditators and novices during yoga nidra practice

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines.

Viscoelasticity of diverse biological samples quantified by Acoustic Force Microrheology (AFMR)

In the context of soft matter and cellular mechanics, microrheology - the use of micron-sized particles to probe the frequency-dependent viscoelastic response of materials - is widely used to shed light onto the mechanics and dynamics of molecular structures. Here we present the implementation of active microrheology in an Acoustic Force Spectroscopy setup (AFMR), which combines multiplexing with the possibility of probing a wide range of forces ( ~ pN to ~nN) and frequencies (0.01-100 Hz). To demonstrate the potential of this approach, we perform active microrheology on biological samples of increasing complexity and stiffness: collagen gels, red blood cells (RBCs), and human fibroblasts, spanning a viscoelastic modulus range of five orders of magnitude. We show that AFMR can successfully quantify viscoelastic properties by probing many beads with high single-particle precision and reproducibility. Finally, we demonstrate that AFMR to map local sample heterogeneities as well as detect cellular responses to drugs.

Cost-effectiveness of resistance-guided therapy for Mycoplasma genitalium in Australia

The recommended first-line treatment for Mycoplasma genitalium infections is azithromycin. However, the prevalence of macrolide resistance for M. genitalium has increased to more than 50% worldwide. In 2013, Australia introduced a resistance-guided therapy (RGT) strategy to manage M. genitalium infections. This study assesses the cost-effectiveness of the RGT approach compared to no RGT (i.e., without macrolide resistance profile test) in women, men who have sex with men (MSM), and men who have sex with women (MSW) in Australia. We constructed dynamic transmission models of M. genitalium infections in women, MSM, and MSW in Australia, each with a population of 100,000. These models compared the costs and quality-adjusted life-years (QALYs) gained between RGT and no RGT scenarios from a healthcare perspective over ten years. All costs are reported in 2022 Australian dollars (Australian $). In our model, RGT is cost saving in women and MSM, with the incremental net monetary benefit of $1.3 million and $17.9 million, respectively. In MSW, the RGT approach is not cost-effective, with an incremental cost-effectiveness ratio of -$106.96 per QALY gained. RGT is cost saving compared to no RGT for M. genitalium infections in women and MSM, supporting its adoption as the national management strategy for these two population groups.

Solar light driven atomic and electronic transformations in a plasmonic Ni@NiO/NiCO3 photocatalyst revealed by ambient pressure X-ray photoelectron spectroscopy

This work employs ambient pressure X-ray photoelectron spectroscopy (APXPS) to delve into the atomic and electronic transformations of a core-shell Ni@NiO/NiCO3 photocatalyst - a model system for visible light active plasmonic photocatalysts used in water splitting for hydrogen production. This catalyst exhibits reversible structural and electronic changes in response to water vapor and solar simulator light. In this study, APXPS spectra were obtained under a 1 millibar water vapor pressure, employing a solar simulator with an AM 1.5 filter to measure spectral data under visible light illumination. The in situ APXPS spectra indicate that the metallic Ni core absorbs the light, exciting plasmons, and creates hot electrons that are subsequently utilized through hot electron injection in the hydrogen evolution reaction (HER) by NiCO3. Additionally, the data show that NiO undergoes reversible oxidation to NiOOH in the presence of water vapor and light. The present work also investigates the contribution of carbonate and its involvement in the photocatalytic reaction mechanism, shedding light on this seldom-explored aspect of photocatalysis. The APXPS results highlight the photochemical reduction of carbonates into -COOH, contributing to the deactivation of the photocatalyst. This work demonstrates the APXPS efficacy in examining photochemical reactions, charge transfer dynamics and intermediates in potential photocatalysts under near realistic conditions.

The conceptualisation and measurement of engagement in digital health

Digital tools are an increasingly important component of healthcare, but their potential impact is commonly limited by a lack of user engagement. Digital health evaluations of engagement are often restricted to system usage metrics, which cannot capture a full understanding of how and why users engage with an intervention. This study aimed to examine how theory-based, multifaceted measures of engagement with digital health interventions capture different components of engagement (affective, cognitive, behavioural, micro, and macro) and to consider areas that are unclear or missing in their measurement. We identified and compared two recently developed measures that met these criteria (the Digital Behaviour Change Intervention Engagement Scale and the TWente Engagement with Ehealth Technologies Scale). Despite having similar theoretical bases and being relatively strongly correlated, there are key differences in how these scales aim to capture engagement. We discuss the implications of our analysis for how affective, cognitive, and behavioural components of engagement can be conceptualised and whether there is value in distinguishing between them. We conclude with recommendations for the circumstances in which each scale may be most useful and for how future measure development could supplement existing scales.

Healthy tissue metabolism assessed by [18F]FDG PET/CT as a marker of prognosis and adverse events in advanced Hodgkin lymphoma patients

The aim of the study was to assess healthy tissue metabolism (HTM) using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) during chemotherapy in Hodgkin lymphoma (HL) and the association of HTM with baseline metabolic tumour volume (MTV), haematological parameters, adverse events (AEs), early response and progression-free survival (PFS). We retrospectively identified 200 patients with advanced HL from the RATHL trial with [18F]FDG-PET/CT before (PET0) and following 2 cycles of chemotherapy (PET2). [18F]FDG-uptake was measured in bone marrow (BM), spleen, liver and mediastinal blood pool (MBP). Deauville score (DS) 1-3 was used to classify responders and DS 4-5, non-responders. [18F]FDG-uptake decreased significantly in BM and spleen and increased in liver and MBP at PET2 (all p < 0.0001), but was not associated with MTV. Higher BM uptake at PET0 was associated with lower baseline haemoglobin and higher absolute neutrophil counts, platelets, and white blood cells. High BM, spleen, and liver uptake at PET0 was associated with neutropenia after cycles 1-2. BM uptake at PET0 was associated with treatment failure at PET2 and non-responders with higher BM uptake at PET2 had significantly inferior PFS (p = 0.023; hazard ratio = 2.31). Based on these results, we concluded that the change in HTM during chemotherapy was most likely a direct impact of chemotherapy rather than a change in MTV. BM uptake has prognostic value in HL.

Impact of Prenatal Care on Newborn Complications for Infants with Cleft Lip with or Without Cleft Palate

OBJECTIVE

To determine the association between prenatal care and cleft lip with or without cleft palate (CL ± P) and examine differences in newborn complications among infants diagnosed with CL ± P as a function of prenatal care.

DESIGN

Population-based retrospective cohort study.

SETTING

2018 United States National Vital Statistics System-Natality component (NVSS-N) was used to examine nationwide birth certificate data.

PARTICIPANTS

3,414,338 infants from the 2018 National Vital Statistics System, of which 1,699 had CL ± P.

MAIN OUTCOME MEASURE

Diagnosis of CL ± P and presence of newborn complications as a function of prenatal care.

RESULTS

Significant differences were found among various infant- and mother-specific variables when baseline comparisons were made between infants with and without CL ± P. After controlling for baseline differences, results indicated decreased odds of a diagnosis of CL ± P in cases where overall adequate prenatal care was obtained (OR = .841; 95% CI .757, .934), including prenatal care beginning in the 1st trimester (OR = .839; 95% CI .750, .939) and an adequate number of prenatal visits received (OR = .864; 95% CI .764, .976). Of infants with CL ± P, reduced odds of the infant admitted to the neonatal intensive care unit (OR = .777; 95% CI .613, .985) or transferred (OR = .601; 95% CI .407, .888) were apparent when adequate prenatal care was received.

CONCLUSION

Results suggest adequate prenatal care not only reduces the likelihood of CL ± P in infants but may also decrease the severity of negative outcomes in infants diagnosed with CL ± P. These findings emphasize necessity for adequate prenatal care.

The Effect of Simulated Obstructive Apneas on Mechanical Characteristics of Lower Airways in Individuals with Asthma

Increased negative intrathoracic pressure that occurs during pharyngeal obstruction can increase thoracic fluid volume that may contribute to lower airway narrowing in individuals with obstructive sleep apnea (OSA) and asthma. Our previous study showed that fluid accumulation in the thorax induced by simulated OSA can increase total respiratory resistance. However, the effect of fluid shift on lower airway narrowing has not been investigated. To examine the effect of fluid accumulation in the thorax on the resistance of the lower airway. Non-asthma participants and individuals with (un)controlled asthma were recruited and underwent a single-day experiment. A catheter with six pressure sensors was inserted through the nose to continuously measure pressure at different sites of the airway, while a pneumotachograph was attached to a mouthpiece to record airflow. To simulate obstructive apneas, participants performed 25 Mueller maneuvers (MMs) while lying supine. Using the recordings of pressure sensor and airflow, total respiratory (RT), lower respiratory components (RL), and upper airway (RUA) resistances were calculated before and after MMs. Generalized estimation equation method was used to find the predictors of RL among variables including age, sex, body mass index, and the effect of MMs and asthma. Eighteen participants were included. Performing MMs significantly increased RT (2.23 ± 2.08 cmH2O/L/s, p = 0.003) and RL (1.52 ± 2.00 cmH2O/L/s, p = 0.023) in participants with asthma, while only RL was increased in non-asthma group (1.96 ± 1.73 cmH2O/L/s, p = 0.039). We found the model with age, and the effect of MMs and asthma severity generated the highest correlation (R2 = 0.69, p < 0.001). We provide evidence that fluid accumulation in the thorax caused by excessive intrathoracic pressure increases RL in both non-asthma and asthma groups. The changes in RL were related to age, having asthma and the effect of simulated OSA. This can explain the interrelationship between OSA and asthma.

Recent advancements in the physiological, genetic, and genomic research on Rhododendrons for trait improvement

High species diversity, hybridization potential, broad geographical dispersal range and ornamental characteristics (i.e., attractive size, shape, structure, flowers, and evergreen) have fetched a good international market for Rhododendron. However, most species are restricted to specific geographic areas due to their habitat specificity in acidic soil and cold climates, resulting many species being classified under threat categories of the IUCN. In this review, advances in research on Rhododendron for improvement to floral display quality and stress resistance have been described. The low genetic barrier among species has created opportunities for extensive hybridization and ploidy alteration for introducing quality and adaptive traits during the development of new varieties. Recent technological advances have supported investigations into the mechanism of flower development, as well as cold tolerance and pathogen resistance mechanisms in the Rhododendron. However, most of the species have limited adaptability to drought, line-tolerance, pathogen resistance, and high-temperature conditions and this resistance ability present in few species largely remains unexplored. Additionally, the available genetic diversity and genomic information on species, and possibilities for their application in molecular breeding have been summarized. Overall, genomic resource data are scarce in the majority of the members of this genus. Finally, various research gaps such as genetic mapping of quality traits, understanding the molecular mechanism of quality-related traits and genomic assortment in Rhododendron members have been discussed in the future perspective section.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04006-6.

Supported of gold nanoparticles on carboxymethyl lignin modified magnetic nanoparticles as an efficient catalyst for reduction of nitroarenes and treatment of human melanoma

This article represents the synthesis and characterizations of Au NPs immobilized and carboxymethyl lignin (CML) modified Fe3O4 nanoparticles (Fe3O4@CML/Au NPs) following a bio-inspired protocol without the participation of any toxic and harmful reductant or stabilizers. Following various physicochemical methodologies, such as FT-IR, FE-SEM, TEM, EDX, XRD, VSM, and ICP-OES, the textural characteristics and different structural aspects were evaluated. The Fe3O4@CML/Au NPs nanocomposite was subsequently explored towards the catalytic reduction of diverse aromatic nitro functions using green conditions. An excellent yield were achieved within very short reaction time. Nine recycling runs of the nanocatalyst were completed without a discernible loss of catalytic activity, thanks to its easy magnetic recovery. The DPPH assay was carried out to examine the antioxidant effectiveness. The Fe3O4@CML/Au NPs nanocomposite inhibited half of the DPPH in a 250 μg/mL solution. To measure the anti-human melanoma efficacy of Fe3O4@CML/Au NPs nanocomposite, MTT assay was applied on HT144, MUM2C, IPC-298 and SKMEL24 cell lines. Fe3O4@CML/Au NPs nanocomposite had high anti-human melanoma efficacy on above tumor cells. The best finding of anti-human melanoma properties of Fe3O4@CML/Au NPs nanocomposite was seen in the case of the SKMEL24 cell line. The IC50 of Fe3O4@CML/Au NPs nanocomposite was 137, 145, 185, and 125 μg/mL against HT144, MUM2C, IPC-298 and SKMEL24 cells, respectively. This research exhibited remarkable anti-human melanoma and antioxidant efficacies of Fe3O4@CML/Au NPs nanocomposite in the in vitro condition.

Multimodal evaluation of drug antibacterial activity reveals cinnamaldehyde analog anti-biofilm effects against Haemophilus influenzae

Biofilm formation by the pathobiont Haemophilus influenzae is associated with human nasopharynx colonization, otitis media in children, and chronic respiratory infections in adults suffering from chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD). β-lactam and quinolone antibiotics are commonly used to treat these infections. However, considering the resistance of biofilm-resident bacteria to antibiotic-mediated killing, the use of antibiotics may be insufficient and require being replaced or complemented with novel strategies. Moreover, unlike the standard minimal inhibitory concentration assay used to assess antibacterial activity against planktonic cells, standardization of methods to evaluate anti-biofilm drug activity is limited. In this work, we detail a panel of protocols for systematic analysis of drug antimicrobial effect on bacterial biofilms, customized to evaluate drug effects against H. influenzae biofilms. Testing of two cinnamaldehyde analogs, (E)-trans-2-nonenal and (E)-3-decen-2-one, demonstrated their effectiveness in both H. influenzae inhibition of biofilm formation and eradication or preformed biofilms. Assay complementarity allowed quantifying the dynamics and extent of the inhibitory effects, also observed for ampicillin resistant clinical strains forming biofilms refractory to this antibiotic. Moreover, cinnamaldehyde analog encapsulation into poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles allowed drug vehiculization while maintaining efficacy. Overall, we demonstrate the usefulness of cinnamaldehyde analogs against H. influenzae biofilms, present a test panel that can be easily adapted to a wide range of pathogens and drugs, and highlight the benefits of drug nanoencapsulation towards safe controlled release.

Heat-killed Limosilactobacillus reuteri ATCC PTA 6475 prevents bone loss in ovariectomized mice: A preliminary study

Osteoporosis is an important health problem that occurs due to an imbalance between bone formation and resorption. Hormonal deficiency post-menopause is a significant risk factor. The probiotic Limosilactobacillus reuteri has been reported to prevent ovariectomy (Ovx)-induced bone loss in mice and reduce bone loss in postmenopausal women. Despite the numerous health benefits of probiotics, as they are live bacteria, the administration is not risk-free for certain groups (e.g., neonates and immunosuppressed patients). We evaluated the effects of L. reuteri (ATCC PTA 6475) and its heat-killed (postbiotic) form on Ovx-induced bone loss. Adult female mice (BALB/c) were randomly divided into four groups: group C-control (sham); group OVX-C-Ovx; group OVX-POS-Ovx + heat-killed probiotic; group OVX-PRO-Ovx + probiotic. L. reuteri or the postbiotic was administered to the groups (1.3x109 CFU/day) by gavage. Bacterial morphology after heat treatment was accessed by scanning electron microscopy (SEM). The treatment started one week after Ovx and lasted 28 days (4 weeks). The animals were euthanized at the end of the treatment period. Bone microarchitecture and ileum Occludin and pro-inflammatory cytokines gene expression were evaluated by computed microtomography and qPCR techniques, respectively. The Ovx groups had lower percentage of bone volume (BV/TV) and number of bone trabeculae as well as greater total porosity compared to the control group. Treatment with live and heat-killed L. reuteri resulted in higher BV/TV and trabecular thickness than the Ovx group. The heat treatment caused some cell surface disruptions, but its structure resembled that of the live probiotic in SEM analysis. There were no statistical differences in Occludin, Il-6 and Tnf-α gene expression. Both viable and heat-killed L. reuteri prevented bone loss on ovariectomized mice, independently of gut Occludin and intestinal Il-6 and Tnf-α gene expression.

Risk of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Among Patients with Type 2 Diabetes Mellitus on Anti-Hyperglycemic Medications

Background

Observed activity of metformin in reducing the risk of severe COVID-19 suggests a potential use of the anti-hyperglycemic in the prevention of post-acute sequelae of SARS-CoV-2 infection (PASC). We assessed the 3-month and 6-month risk of PASC among patients with type 2 diabetes mellitus (T2DM) comparing metformin users to sulfonylureas (SU) or dipeptidyl peptidase-4 inhibitors (DPP4i) users.

Methods

We used de-identified patient level electronic health record data from the National Covid Cohort Collaborative (N3C) between October 2021 and April 2023. Participants were adults ≥ 18 years with T2DM who had at least one outpatient healthcare encounter in health institutions in the United States prior to COVID-19 diagnosis. The outcome of PASC was defined based on the presence of a diagnosis code for the illness or using a predicted probability based on a machine learning algorithm. We estimated the 3-month and 6-month risk of PASC and calculated crude and weighted risk ratios (RR), risk differences (RD), and differences in mean predicted probability.

Results

We identified 5596 (mean age: 61.1 years; SD: 12.6) and 1451 (mean age: 64.9 years; SD 12.5) eligible prevalent users of metformin and SU/DPP4i respectively. We did not find a significant difference in risk of PASC at 3 months (RR = 0.86 [0.56; 1.32], RD = -3.06 per 1000 [-12.14; 6.01]), or at 6 months (RR = 0.81 [0.55; 1.20], RD = -4.91 per 1000 [-14.75, 4.93]) comparing prevalent users of metformin to prevalent users of SU/ DPP4i. Similar observations were made for the outcome definition using the ML algorithm.

Conclusion

The observed estimates in our study are consistent with a reduced risk of PASC among prevalent users of metformin, however the uncertainty of our confidence intervals warrants cautious interpretations of the results. A standardized clinical definition of PASC is warranted for thorough evaluation of the effectiveness of therapies under assessment for the prevention of PASC.

Synthesis, theoretical studies, antibacterial, and antibiofilm activities of novel azo-azomethine chelates against the pathogenic bacterium Proteus mirabilis

2-((1-(4-((2,4,6-trioxohexahydropyrimidin-5-yl)diazenyl) phenyl) ethylidene) amino) benzoic acid (H3L), and its V(IV), Co(II), Ni(II), Cu(II), Pd(II) and Ag(I) chelates were synthesized. They were defined using multiple spectral and analytical techniques. With the exception of Ag(I) chelate, all chelates possessed non-electrolytic character. Square pyramidal shape was proposed for V(IV) chelate and Square planar for the other chelates. The analysis of functional group bands of H3L and its coordination compounds alludes that H3L chelated as neutral tetradentate via nitrogen atoms of azo and azomethine groups, oxygen atom of carbonyl of barbituric acid and OH of the carboxylic group. TG/DTG predicted the thermal behaviors of all compounds. The antibacterial activity of H3L and its coordination compounds was conducted against Proteus mirabilis at concentrations of 250, 500, and 1000 µg/mL. Ag(I) at 1000 µg/mL, showed the most inhibiting potency against P. mirabilis and registered zone of inhibition of 28.33 ± 0.84 mm and highest biofilm inhibition of 70.31%. At 50 Gy of gamma irradiation, the reducing effect of Ag(I) chelate was improved. The protein interruption of P. mirabilis was greatly interrupted by increasing the concentration of the chaletes. Also, Ag(I) showed the highest cytotoxicity with IC50 value of 11.5 µg/ mL. The novelty of this study is the synthesis of a new azo-Schiff base and this is almost the first publication of the effect of azo-Schiff ligands against that bacterial strain P. mirabilis.

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

Comparison of inflammatory markers, coagulation indicators and outcomes between influenza and COVID-19 infection amongst children: A systematic review and meta-analysis

Background

Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age group especially in terms of inflammatory markers, coagulation profile and outcomes. Hence, we did this review to compare the inflammatory, coagulation features and outcomes between influenza and COVID-19 infected children.

Methods

Literature search was done in PubMed Central, Scopus, EMBASE, CINAHL, Cochrane library, Google Scholar & ScienceDirect from November 2019 to May 2022. Risk of bias assessment was done through Newcastle Ottawa scale. Meta-analysis was done using random-effects model and the final pooled estimate was reported as pooled odds ratio (OR) or standardized mean difference (SMD) along with 95 % confidence interval (CI) depending on the type of outcome.

Results

About 16 studies were included with most studies having higher risk of bias. Influenza paediatric patients had significantly higher erythrocyte sedimentation rate (ESR) (pooled SMD = 0.60; 95%CI: 0.30-0.91; I2 = 0 %), lactate dehydrogenase (LDH) (pooled SMD = 2.01; 95%CI: 0.37-3.66; I2 = 98.4 %) and prothrombin time (PT) (pooled SMD = 2.12; 95%CI: 0.44-3.80; I2 = 98.3 %) when compared to paediatric COVID-19 patients. There was no significant difference in terms of features like CRP, procalcitonin, serum albumin, aPTT, mortality and need for mechanical ventilation.

Conclusion

Inflammatory markers like ESR, LDH and PT was significantly higher in influenza patients when compared to COVID-19 in children, while rest of the markers and adverse clinical outcomes were similar between both the groups. Identification of these biomarkers has helped in understanding the distinctness of COVID-19 and influenza virus and develop better management strategies.

Computer-assisted decision support for the usage of preventive antibacterial therapy in children with febrile pyelonephritis: A preliminary study

Urinary tract infection (UTI) is one of the most common infectious diseases among children, but there is controversy regarding the use of preventive antibiotics for children first diagnosed with febrile pyelonephritis. To the best of our knowledge, no studies have addressed this issue by the deep learning technology. Therefore, in the current study, we conducted a study using Tc 99 m - DMSA renal static imaging data to investigate the need for preventive antibiotics on children first diagnosed with febrile pyelonephritis under 2 years old. The self-collected dataset comprised 64 children who did not require preventive antibiotic treatments and 112 children who did. Using several classic deep learning models, we verified that it is feasible to screen whether the first diagnosed children with febrile pyelonephritis require preventive antibacterial therapy, achieving a graded diagnosis. With the AlexNet model, we obtained accuracy of 84.05%, sensitivity of 81.71% and specificity of 86.70%, respectively. The experimental results indicate that deep learning technology could provide a new avenue to implement computer-assisted decision support for the diagnosis of the febrile pyelonephritis.

Functional annotation and meta-analysis of maize transcriptomes reveal genes involved in biotic and abiotic stress

BACKGROUND

Environmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress.

RESULTS

We mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments.

CONCLUSIONS

These results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB).

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Autonomous generation of single photon emitting materials

The utilization of machine learning in Materials Science underscores the critical importance of the quality and quantity of data in training models effectively. Unlike fields such as image processing and natural language processing, there is limited availability of atomistic datasets, leading to biases in training data. Particularly in the domain of materials discovery, there exists an issue of continuity in atomistic datasets. Experimental data sourced from literature and patents is usually only available for favorable data, resulting in bias in the training dataset. This study focuses on developing a SMILES-based model for generating synthetic datasets of quantum materials using a variational autoencoder. This study centers on the generation of a synthetic dataset of quantum materials specifically for quantum sensing applications, with a focus on two-level quantum molecules that exhibit a dipole blockade. The proposed technique offers an improved sampling algorithm by incorporating newly generated data into the sampling algorithm to create a more normally distributed dataset. Through this technique, the study was able to generate over 1 000 000 candidate quantum materials from a small dataset of only 8000 materials. The generated dataset identified several iodine-containing molecules as promising single photon emitting materials for potential quantum sensing applications.

A microphysiological system for handling graphene related materials under flow conditions

The field of nanotechnology has developed rapidly in recent decades due to its broad applications in many industrial and biomedical fields. Notably, 2D materials such as graphene-related materials (GRMs) have been extensively explored and, as such, their safety needs to be assessed. However, GRMs tend to deposit quickly, present low stability in aqueous solutions, and adsorb to plastic materials. Consequently, traditional approaches based on static assays facilitate their deposition and adsorption and fail to recreate human physiological conditions. Organ-on-a-chip (OOC) technology could, however, solve these drawbacks and lead to the development of microphysiological systems (MPSs) that mimic the microenvironment present in human tissues. In light of the above, in the present study a microfluidic system under flow conditions has been optimised to minimise graphene oxide (GO) and few-layer graphene (FLG) adsorption and deposition. For that purpose, a kidney-on-a-chip was developed and optimised to evaluate the effects of exposure to GO and FLG flakes at a sublethal dose under fluid flow conditions. In summary, MPSs are an innovative and precise tool for evaluating the effects of exposure to GRMs and other type of nanomaterials.

Supplementing infant milk formula with a multi-strain synbiotic and osteopontin enhances colonic microbial colonization and modifies jejunal gene expression in lactating piglets

A total of ninety-six weaned piglets were assigned to four dietary treatments in a 2 × 2 design. The treatments included: a standard milk formula (CTR); CTR + probiotics (6.4 × 108 cfu L-1Bifidobacterium longum subsp. infantis CECT 7210 and 1.1 × 108 cfu L-1Lactobacillus rhamnosus NH001) + prebiotics (galacto-oligosaccharides 4.36 g L-1 and human-milk-oligosaccharide 0.54 g L-1) (SYN); CTR + osteopontin (0.43 g L-1) (OPN); and CTR + SYN + OPN (CON). Daily records including feed intake, body weight, and clinical signs, were maintained throughout the 15-day trial. At the end of the study samples from blood, digestive content, and gut tissues were collected to determine serum TNF-α, intestinal fermentative activity (SCFA and ammonia), colonic microbiota (16S rRNA Illumina-MiSeq), histomorphology, and jejunal gene expression (Open-Array). No statistical differences were found in weight gain; however, the animals supplemented with osteopontin exhibited higher feed intake. In terms of clinical signs, synbiotic supplementation led to a shorter duration of diarrhoea episodes. Regarding gut health, the sequenced faecal microbiota revealed better control of potentially dysbiotic bacteria with the CON diet at day 15. In the colon compartment, a significant increase in SCFA concentration, a decrease in ammonia concentration, and a significant decrease in intraepithelial lymphocyte counts were particularly observed in CON animals. The supplemented diets were also associated with modified jejunal gene expression. The synbiotic combination was characterized by the upregulation of genes related to intestinal maturation (ALPI, SI) and nutrient transport (SLC13A1, SLC15A1, SLC5A1, SLC7A8), and the downregulation of genes related to the response to pathogens (GBP1, IDO, TLR4) or the inflammatory response (IDO, IL-1β, TGF-β1). Osteopontin promoted the upregulation of a digestive function gene (GCG). Correlational analysis between the microbiota population and various intestinal environmental factors (SCFA concentration, histology, and gene expression) proposes mechanisms of communication between the gut microbiota and the host. In summary, these results suggest an improvement in the colonic colonization process and a better modulation of the immune response when milk formula is supplemented with the tested synbiotic combined with osteopontin, benefiting from a synergistic effect.

Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications

Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.

Cerebral blood flow and structural connectivity after working memory or physical training in paediatric cancer survivors - Exploratory findings

Paediatric cancer survivors often suffer from cognitive long-term difficulties. Consequently, strengthening cognition is of major clinical relevance. This study investigated cerebral changes in relation to cognition in non-brain tumour paediatric cancer survivors after working memory or physical training compared to a control group. Thirty-four children (≥one-year post-treatment) either underwent eight weeks of working memory training (n = 10), physical training (n = 11), or a waiting period (n = 13). Cognition and MRI, including arterial spin labelling and diffusion tensor imaging, were assessed at three time points (baseline, post-training, and three-month follow-up). Results show lower cerebral blood flow immediately after working memory training (z = -2.073, p = .038) and higher structural connectivity at the three-month follow-up (z = -2.240, p = .025). No cerebral changes occurred after physical training. Short-term changes in cerebral blood flow correlated with short-term changes in cognitive flexibility (r = -.667, p = .049), while long-term changes in structural connectivity correlated with long-term changes in working memory (r = .786, p = .021). Despite the caution given when interpreting data from small samples, this study suggests a link between working memory training and neurophysiological changes. Further research is needed to validate these findings.

Meticulous catheter care and aseptic approach reduce catheter-related bloodstream infections significantly in hemodialysis patients: A 5-year single center study

AIM

The use of central venous catheters as hemodialysis vascular access is a major contributor to high bloodstream infection rate. In our dialysis unit in Shenzhen Guangdong Province China, we have developed and used our own dialysis catheter care protocol since May 2013 with good results. In this study, we would like to share our experience with the other units.

METHODS

We have undertaken a 5-year retrospective analysis to determine our tunneled dialysis catheter-related blood stream infection rate by adding the number of infections divided by total number of catheter days × 1000. The results were compared with another study carried out in Henan Province China. Demographic data were summarized using descriptive statistics. Continuous and categorical variables were compared using t-test and χ2 test respectively.

RESULTS

Between 2017 and 2021, a total of 216 tunneled dialysis catheters were managed by following our own dialysis access pathway and catheter care protocol. The tunneled dialysis catheter-related bloodstream infection rate was 0.0229 per 1000 catheter days in the 5-year period.

CONCLUSION

Comparing with other published studies in China, our unit has achieved a very low rate of tunneled dialysis catheter-related bloodstream infection which has been sustained over time. This paper explores how our protocol and implementation might have contributed to the results.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

OBJECTIVE

Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.

METHOD

A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.

RESULTS

Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.

CONCLUSIONS

The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images

INTRODUCTION

The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN).

MATERIAL AND METHOD

MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia.

RESULTS

The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy.

CONCLUSION

The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

Learning the mechanisms of network growth

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.

Hospitalization Endpoint in Clinical Trials of Outpatient Settings: using Anti-SARS-COV-2 Therapy as an Example

Purpose

In response to the COVID-19 pandemic, the World Health Organization (WHO) developed a set of outcome measures for trials primarily aimed at hospitalised patients. However, a gap exists in defining outcome standards for non-hospitalised patients. Therefore, this study aims to discuss hospitalisation as a primary outcome in outpatient trials and its potential pitfalls, specifically focusing on trials related to anti-SARS-COV-2 therapy.

Methods

In this narrative review, researchers thoroughly searched MEDLINE and ClinicalTrials.gov from January 2020 to December 2022, targeting Phase III randomized controlled trials involving outpatients with mild-to-moderate COVID-19. The trials were specifically related to anti-SARS-COV-2 monoclonal antibodies or antiviral agents. The study collected essential data, including the type of intervention, comparator, primary objective, primary endpoint, and the use of estimands in the trial.

Results

The search identified 12 trials that evaluated the efficacy of anti-SARS COV-2 therapies in a predefined population. Three studies used hospitalisation and death as primary endpoints in high-risk patients receiving monoclonal antibodies. Nine studies assessed the efficacy of several antiviral agents: four trials used hospitalisation and death as the main endpoints, while others used different measures such as virologic measures using the Reverse Transcription-Polymerase Chain Reaction test (RT-PCR), the eight-point WHO ordinal scale, symptom alleviation by Day 7 and time to clinical response.

Conclusion

Choosing hospitalization as an endpoint may provide meaningful data such as the cost-effectiveness ratio of a drug. However, different hospital utilisation patterns and investigator decisions could bias clinical outcomes if no specific criteria are considered. Therefore, investigators should have clear criteria for determining variables that influence this measure.

A new MOF-based modified adsorbent for the efficient removal of Hg(ii) ions from aqueous media: isotherms and kinetics

Herein, a new MOF-based modified adsorbent for the efficient removal of Hg(ii) ions from water media was successfully prepared. Initially, a MOF nanocomposite was synthesized and applied as an efficient adsorbent for the removal of the target heavy metal ion. Following the synthesis, the MOF-based modified adsorbent was identified and characterized by SEM, XRD and FT-IR analytical instruments. The impact of several key variables such as pH of aqueous solution, adsorbent dosage, contact time, and initial concentration of the analyte of interest on the adsorption efficiency was also investigated in detail. Under the optimal conditions established (pH, 3; dose of adsorbent, 0.4 g L-1; contact time, 40 min and the analyte's concentration of 1 mg L-1) the removal efficiency of 96.3% for Hg(ii) was obtained. The results of the studies on the isotherm and kinetics of adsorption revealed that the adsorption process of Hg(ii) matched with the Langmuir isotherm (R2 > 0.990) and the pseudo 2nd-order kinetic models (R2 > 0.998). Additionally, reuse of the applied adsorbent for five consecutive tests exhibited a small percentage of drop (about 8%) in the removal efficiency of the target ion. Finally, the results indicated that the MOF-based modified compound could be potentially applied as a highly efficacious adsorbent for the discharge of Hg(ii) from aquatic media.

Plasma-derived extracellular matrix for xenofree and cost-effective organoid modeling for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) causes significant cancer mortality worldwide. Cancer organoids can serve as useful disease models by high costs, complexity, and contamination risks from animal-derived products and extracellular matrix (ECM) that limit its applications. On the other hand, synthetic ECM alternatives also have limitations in mimicking native biocomplexity. This study explores the development of a physiologically relevant HCC organoid model using plasma-derived extracellular matrix as a scaffold and nutritive biomatrix with different cellularity components to better mimic the heterogenous HCC microenvironment. Plasma-rich platelet is recognized for its elevated levels of growth factors, which can promote cell proliferation. By employing it as a biomatrix for organoid culture there is a potential to enhance the quality and functionality of organoid models for diverse applications in biomedical research and regenerative medicine and to better replicate the heterogeneous microenvironment of HCC.

METHOD

To generate the liver cancer organoids, HUH-7 hepatoma cells were cultured alone (homogenous model) or with human bone marrow-derived mesenchymal stromal cells and human umbilical vein endothelial cells (heterogeneous model) in plasma-rich platelet extracellular matrix (ECM). The organoids were grown for 14 days and analyzed for cancer properties including cell viability, invasion, stemness, and drug resistance.

RESULTS

HCC organoids were developed comprising HUH-7 hepatoma cells with or without human mesenchymal stromal and endothelial cells in plasma ECM scaffolds. Both homogeneous (HUH-7 only) and heterogeneous (mixed cellularity) organoids displayed viability, cancer hallmarks, and chemoresistance. The heterogeneous organoids showed enhanced invasion potential, cancer stem cell populations, and late-stage HCC genetic signatures versus homogeneous counterparts.

CONCLUSION

The engineered HCC organoids system offers a clinically relevant and cost-effective model to study liver cancer pathogenesis, stromal interactions, and drug resistance. The plasma ECM-based culture technique could enable standardized and reproducible HCC modeling. It could also provide a promising option for organoid culture and scaling up.

COVID‑19 detection from chest X-ray images using transfer learning

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

Neutrophil lymphocyte ratio and heart type fatty acid binding protein as a prognostic marker in Myocardial infarction within 48 h of admission

BACKGROUND

The neutrophil to lymphocyte ratio (NLR) is a measure of systemic inflammation, whereas Heart type fatty acid protein (HFABP) is a cytosolic protein released early after acute coronary syndrome (ACS). The aim of this research study is to determine whether NLR and H-FAB are useful in predicting the prognosis in patients with ST segment elevation myocardial infarction (STEMI) 48 h after admission. This is a prospective observational study conducted on 97 patients who had been admitted to emergency room with ST-elevation myocardial infarction in their ECG in a tertiary care centre of south India. The neutrophil-lymphocyte ratio was measured at the time of admission, 24th hour and 48th hour, and then compared with the outcome. To determine their significance in the MI episode, troponin-I and H-FABP were also measured.

RESULTS

A significant correlation was found in the final outcomes of patients and the NLR at the time of admission and at 48 h (p = 0.01). Additionally, a substantial correlation between NLR and various degrees of LV dysfunction was also observed (p = 0.01). H-FABP was found to be positive in all 97 of the patients examined, whereas Troponin-I was only found to be positive in 56.7%.

CONCLUSION

The study's findings, indicated strong correlations between NLR and LVEF, indicated that NLR might serve as an early predictor of cardiac events which could be either poor prognosis or higher mortality. This research found that H-FABP may serve as an early MI diagnostic marker.

Phylogenetic insights into the genetic legacies of Hungarian-speaking communities in the Carpathian Basin

This study focuses on exploring the uniparental genetic lineages of Hungarian-speaking minorities residing in rural villages of Baranja (Croatia) and the Zobor region (Slovakia). We aimed to identify ancestral lineages by examining genetic markers distributed across the entire mitogenome and on the Y-chromosome. This allowed us to discern disparities in regional genetic structures within these communities. By integrating our newly acquired genetic data from a total of 168 participants with pre-existing Eurasian and ancient DNA datasets, our goal was to enrich the understanding of the genetic history trajectories of Carpathian Basin populations. Our findings suggest that while population-based analyses may not be sufficiently robust to detect fine-scale uniparental genetic patterns with the sample sizes at hand, phylogenetic analysis of well-characterized Y-chromosomal Short Tandem Repeat (STR) data and entire mitogenome sequences did uncover multiple lineage ties to far-flung regions and eras. While the predominant portions of both paternal and maternal DNA align with the East-Central European spectrum, rarer subhaplogroups and lineages have unveiled ancient ties to both prehistoric and historic populations spanning Europe and Eastern Eurasia. This research augments the expansive field of phylogenetics, offering critical perspectives on the genetic constitution and heritage of the communities in East-Central Europe.

Deep learning for determining the difficulty of endodontic treatment: a pilot study

BACKGROUND

To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.

METHODS

A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the "simple assessment" criteria from the American Association of Endodontists' case difficulty assessment form in the Endocase application. A classification task labeled cases as "easy" or "hard", while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set.

RESULTS

The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability.

CONCLUSION

This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

Encapsulation of grape seed oil in oil-in-water emulsion using multilayer technology: Investigation of physical stability, physicochemical and oxidative properties of emulsions under the influence of the number of layers

Many studies have shown that grape seed oil (GSO) is one of the vegetable fats that are plentiful in essential fatty acids and can be used as a fat substitute or to modify fat in food products to reduce saturated fatty acids. However, due to its low solubility and high sensitivity to oxidation, it is necessary to develop delivery systems that can distribute GSO in food more effectively. Recently, the preparation of emulsions using the layer-by-layer (LBL) method has many advantages in delivering lipid-soluble functional compounds. This research was used to check the formation of GSO oil-loaded primary, secondary and tertiary multilayer emulsions stabilized by mixture of anionic gelatin, cationic chitosan, and anionic basil seed gum (BSG) as the aqueous phase at pH 5, prepared using a layer-by-layer electrostatic deposition technique. Multilayer emulsions prepared by GSO and a mixture of gelatin, chitosan, and BSG as the aqueous phase at pH 5. Finally, the effect of the number of layers on the physicochemical properties (particle size, viscosity, turbidity, refractive index, and physical stability) and oxidative stability (peroxide value, thiobarbituric acid value, and fatty acid profile) during the storage time (30 days) at two temperatures 25 °C & 4 °C was investigated. Also, the zeta potential and Fourier transform infrared spectroscopy (FTIR) of mono-layer and multi-layer emulsions were investigated. The results revealed that by increasing the number of layers of multi-layer emulsion of GSO, the stability has improved. Thus, the tertiary emulsion has been more effective than the other two emulsions in maintaining the physicochemical characteristics and stability over time (P < 0.001). Morphological characterization and FTIR spectroscopy results confirmed that gelatin, chitosan, and BSG were successfully loaded into the LBL emulsions. This study can improve the original percept of multilayer emulsions and promulgate their potential applications for the entire encapsulation of essential fatty acids to enrich and prevent peroxide attack.

Uncovering the secret weapons of an invasive plant: The endophytic microbes of Anthemis cotula

Understanding plant-microbe interaction can be useful in identifying the microbial drivers of plant invasions. It is in this context that we explored the diversity of endophytic microbes from leaves of Anthemis cotula, an annual plant that is highly invasive in Kashmir Himalaya. We also tried to establish the role of endophytes in the invasiveness of this alien species. We collected and processed leaf samples from three populations at three different sites. A total of 902 endophytic isolates belonging to 4 bacterial and 2 fungal phyla were recovered that belonged to 27 bacterial and 14 fungal genera. Firmicutes (29.1%), Proteobacteria (24.1%), Ascomycota (22.8%) and Actinobacteria (19%) were dominant across all samples. Plant growth promoting traits, such as Ammonia production, Indole Acetic Acid (IAA) production, Phosphate solubilization and biocontrol activity of these endophytes were also studied and most of the isolates (74.68%) were positive for ammonia production. IAA production, phosphate solubilization and biocontrol activity was present in 39.24%, 36.70% and 20.26% isolates, respectively. Furthermore, Botrytis cinerea, a pathogen of A. cotula in its native range, though present in Kashmir Himalaya does not affect A. cotula probably due to the presence of leaf endophytic microbial antagonists. Our results highlight that the beneficial plant growth promoting interactions and enemy suppression by leaf endophytes of A. cotula, may be contributing to its survival and invasion in the Kashmir Himalaya.

Effects of common co-occurring pesticides (a neonicotinoid and fungicide) on honey bee colony health in a semi-field study

Multiple stressors are linked to declines of insects and important pollinators, such as bees. Recently, interactive effects of multiple agrochemicals on bees have been highlighted, including fungicides, which increase toxicity of neonicotinoid insecticides. Here, we use a semi-field study across two seasons in controlled foraging tunnels to test the effects of a field application of a commercial fungicide product with two active ingredients (pyraclostrobin and metconazole) applied at label rates. We also examine its interactive effects with the neonicotinoid insecticide clothianidin, at a conservative field-realistic dose of 2.23 ppb, on 48 honey bee colonies. We found combined effects of pesticide exposure, including additive 2.93-fold increases in mortality, and an additional effect of increased infestation levels of the ectoparasitic mite, Varroa destructor. Pesticide treatments also reduced colony activity, reduced colony weight, and increased sugar consumption of whole colonies. These findings indicate that typical sublethal exposure levels to common, co-occurring agrochemicals in the field significantly affect the health of whole honey bee colonies, highlighting an unintended consequence of increasing pesticide applications.

Non-specific protection against severe COVID-19 associated to typhoid fever and DTP vaccination

Trained immunity (TRAIM) or the enhanced non-specific immune response after primary stimulation by infection or vaccination is a recent but well-recognized concept. To verify its predictions, our objective was to determine the effects of two bacterial vaccines, typhoid fever (TFV) and diphtheria-tetanus-pertussis (DTP) on the infection, hospitalization and death frequencies associated to COVID-19 in a retrospective study on subjects vaccinated or not with TFV and DTP in the 4 years prior to the start of COVID-19 pandemia in the Basque Country (Spain). The studied outcome records were split into two periods according to COVID-19 vaccination, the pre-vaccination (ACV) from March to December 2020 and the post-vaccination (PCV) from September 2021 to June 2022). In total, 13,673 subjects were vaccinated against TFV and 42,997 against DTP. A total of 2,005,084 individual records were studied in the ACV period and 1,436,693 in the PCV period. The proportion of infection, hospitalization and death associated to COVID-19 among controls in ACV was 4.97 %, 7.14 % and 3.54 %, respectively vs. 7.20 %, 2.24 % and 0.10 % among TFV subjects. Regarding DTP, the proportions were 4.97 %, 7.12 % and 3.58 % for controls and 5.79 %, 5.79 % and 0.80 % for vaccinees. In the PCV period, the proportion of infection, hospitalization and death among controls was 21.89 %, 2.62 % and 0.92 %, respectively vs. 31.19 %, 0.76 %, 0.00 % among TFV. For DTP, infection, hospitalization and death proportions were 21.89 %, 2.62 % and 0.92 %, respectively, among controls vs. 32.03 %, 1.85 % and 0.24 % among vaccinated subjects. The corresponding combined ACV and PCV odds ratios (OR) for SARS-CoV2 infection were 1.505 (95%CI 1.455-1.558; p < 0.0001; reduction -41.85 %) and 1.633 (95%CI 1.603-1.662; p < 0.0001; reduction -51.74 %), for TFV and DTP, respectively. Regarding COVID-19 associated hospitalization, the OR were 0.295 (95%CI 0.220-0.396; p = 0.0001; reduction 69.74 %) and 0.667 (95%CI 0.601-0.741; p = 0.0001; reduction 32.44 %), for TFV and DTP, respectively). COVID-19 associated death OR were 0.016 (95%CI 0.002-0.113, p < 0.0001; reduction 98.38 %) and 0.212 (95%CI 0.161-0.280; p = 0.0001; reduction 78.52 %), for TFV and DTP, respectively. We conclude that TRAIM effects by TFV and DTP vaccination in the four years prior to the pandemic SARS-CoV2 were supported by slightly increased infection rates, but strongly reduced COVID-19 associated hospitalization and death rates.

Applications of magnetic resonance spectroscopy in diagnosis of neurodegenerative diseases: A systematic review

Background

Magnetic resonance spectroscopy (MRS) is an imaging technique used to measure metabolic changes in the tissue. Due to the lack of evidence, MRS is not a priority in diagnosing neurodegenerative diseases because it is a relatively specialized technique that requires specialized equipment and expertise to perform and interpret. This systematic review aimed to present a comprehensive collection of MRS results in the most common neurodegenerative diseases.

Methods

A systematic search of four electronic databases (PubMed, Scopus, Web of Science, and ScienceDirect) was conducted for studies published from 2017 to 2022. Articles that provided specific biomarker levels were selected, and studies that assessed the diseases via treatment, featured MRS applying nuclei other than 1H, or compared different animal models were excluded.

Results

A total of 25 articles, plus 3 articles for extra information in the introduction, were included in this review. Six of the most common neurodegenerative diseases, i.e., Alzheimer's and Parkinson's disease, Huntington chorea, ataxia, multiple sclerosis (MS), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) were examined via MRS. The changes and ratios of N-acetylaspartate (NAA) could be seen in all of these disorders, which could lead to early diagnosis. However, there are other biomarkers, such as Cr and Chon, which can give convincing results.

Discussion

This observational study is the first synthesis of the latest evidence proving metabolic changes during neurodegenerative diseases using MRS as a diagnosis method. The findings indicate decreased N-acetylaspartate (NAA) and NAA/Cr ratios in Alzheimer's disease (AD), Parkinson's disease (PD), ataxias, and MS, reflecting neuronal loss or dysfunction. Increased choline and myo-inositol were noted in some studies, suggesting cell membrane turnover and neuroinflammation. Findings were less consistent for other metabolites like glutamate and gamma-aminobutyric acid. However, there were limitations due to the lack of studies on the same volumes of interest (VOIs) and the small number of participants.

Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery

Purpose

This investigation was conceived to engineer and appraise a pioneering clinical nomogram, crafted to bridge the extant chasm in literature regarding the postoperative risk stratification for deep vein thrombosis (DVT) in the aftermath of lower extremity orthopedic procedures. This novel tool offers a sophisticated and discerning algorithm for risk prediction, heretofore unmet by existing methodologies.

Methods

In this retrospective observational study, clinical records of hospitalized patients who underwent lower extremity orthopedic surgery were collected at the Wuxi TCM Hospital Affiliated to the Nanjing University of Chinese Medicine between Jan 2017 and Oct 2019. The univariate and multivariate analysis with the backward stepwise method was applied to select features for the predictive nomogram. The performance of the nomogram was evaluated with respect to its discriminant capability, calibration ability, and clinical utility.

Result

A total of 5773 in-hospital patients were eligible for the study, with the incidence of deep vein thrombosis being approximately 1 % in this population. Among 31 variables included, 5 of them were identified to be the predictive features in the nomogram, including age, mean corpuscular hemoglobin concentration (MCHC), D-dimer, platelet distribution width (PDW), and thrombin time (TT). The area under the receiver operating characteristic (ROC) curve in the training and validation cohort was 85.9 % (95%CI: 79.96 %-90.04 %) and 85.7 % (95%CI: 78.96 %-90.69 %), respectively. Both the calibration curves and decision curve analysis demonstrated the overall satisfactory performance of the model.

Conclusion

Our groundbreaking nomogram is distinguished by its unparalleled accuracy in discriminative and calibrating functions, complemented by its tangible clinical applicability. This innovative instrument is set to empower clinicians with a robust framework for the accurate forecasting of postoperative DVT, thus facilitating the crafting of bespoke and prompt therapeutic strategies, aligning with the rigorous standards upheld by the most esteemed biomedical journals.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Design of potent tyrosinase inhibiting N-arylated-4-yl-benzamides bearing 2-aminothiazole-triazole bi-heterocycles: mechanistic insight through enzyme inhibition, kinetics and computational studies

By using a convergent methodology, a unique series of N-arylated 4-yl-benzamides containing a bi-heterocyclic thiazole-triazole core was synthesized and the structures of these hybrid molecules, 9a-k, were corroborated through spectral analyses. The in vitro studies of these multi-functional molecules demonstrated their potent mushroom tyrosinase inhibition relative to the standard used. The kinetics mechanism was exposed by lineweaver-burk plots which revealed that, 9c, inhibited mushroom tyrosinase non-competitively by forming an enzyme-inhibitor complex. The inhibition constant Ki calculated from Dixon plots for this compound was 0.016 μM. The computational study was also consistent with the experimental results and these molecules disclosed good results of all scoring functions and interactions, which suggested a good binding to mushroom tyrosinase. So, it was predicted from the inferred results that these molecules might be considered as promising medicinal scaffolds for the diseases associated with the over-expression of this enzyme.

Identification, management and pecuniary impact of major carbon footprint contributor in potato production system of north-west India

Assessment of carbon footprint of a crop is an important component of sustainable crop production, as it helps in framing effectual and viable crop management strategies to minimize ecosystem tampering. Thus, in present investigation carbon footprint of potato production system in different agro-climatic zones viz. undulating plain zone, central plain zone and western plain zone of North-west India were estimated, and compared with the recommended practices of these zones. The carbon footprint was higher in undulating plain zone followed by central plain zone and western plain zone with values being 343, 296 and 220 kg CO2 eq./t tuber yield (TY), sequentially, whereas same were 198 kg CO2 eq./t tuber yield (TY) in case of recommended practices. The social cost of carbon (SCC), that represents economic damage from the CO2 emissions, was also estimated. The integrated net economic balance (net return from yield - SCC) was also better in case of recommended practices. The major sources of emission from potato production system were fertilizer (NPK) application (42 %), irrigation (20 %), seed (14 %), fertilizer production (13 %) and energy use (excluding Irrigation) (5 %). Top most in the list of carbon footprint contributors was fertilizer application which was due to imbalanced application of these, and for getting the clear picture of this imbalance as well as its impact, a new and exclusive index- Relative Imbalance Fertilization Index (RIFIcf) was developed and tested. Carbon footprints were also related to tuber yield and an empirical model was developed that can be used to predict tuber yield on the basis of carbon footprint of potato production system. An increase in tuber yield with increasing carbon footprint was noticed, which became somewhat static at higher emissions. The findings of this investigation provide a clear picture of quantitative GHG emissions due to imbalanced inputs that can be plummeted to some extent if already existing recommendations are followed.