A systematic toxicologic study of polycyclic aromatic hydrocarbons on aquatic organisms via food-web bioaccumulation

Pollution-induced declines in fishery resources restrict the sustainable development of fishery. As a kind of typical environmental pollutant, the mechanism of polycyclic aromatic hydrocarbons (PAHs) facilitating fishery resources declines needs to be fully illustrated. To determine how PAHs have led to declines in fishery resources, a systematic toxicologic analysis of the effects of PAHs on aquatic organisms via food-web bioaccumulation was performed in the Pearl River and its estuary. Overall, PAH bioaccumulation in aquatic organisms was correlated with the trophic levels along food-web, exhibiting as significant positive correlations were observed between PAHs concentration and the trophic levels of fishes in the Pearl River Estuary. Additionally, waterborne PAHs exerted significant direct effects on dietary organisms (P < 0.05), and diet-borne PAHs subsequently exhibited significant direct effects on fish (P < 0.05). However, an apparent block effect was found in dietary organisms (e.g., zooplankton) where 33.49 % of the total system throughput (TST) was retained at trophic level II, exhibiting as the highest PAHs concentration, bioaccumulation factor (BAF), and biomagnification factor (BMF) of ∑15PAHs in zooplankton were at least eight-fold greater than those in fishes in both the Pearl River and its estuary, thereby waterborne PAHs exerted either direct or indirect effects on fishes that ultimately led to food-web simplification. Regardless of the block effect of dietary organisms, a general toxic effect of PAHs on aquatic organisms was observed, e.g., Phe and BaP exerted lethal effects on phytoplankton Chlorella pyrenoidosa and zooplankton Daphnia magna, and decreased reproduction in fishes Danio rerio and Megalobrama hoffmanni via activating the NOD-like receptors (NLRs) signaling pathway. Consequently, an assembled aggregate exposure pathway for PAHs revealed that increases in waterborne PAHs led to bioaccumulation of PAHs in aquatic organisms along food-web, and this in turn decreased the reproductive ability of fishes, thus causing decline in fishery resources.

Facile fluorescence detection of malachite green in fish using molecularly imprinted polymers doped CdTe quantum dots based system

Malachite green (MG) possesses high toxicity, therefore, the detection of MG in fish tissues is of vital importance. A novel core-shell MIPs doped CdTe quantum dots coated silica nanoparticles (CdTe-MIP/SiO2 NPs) were synthesized via a simple one-pot strategy. The materials were characterized carefully. The resulting CdTe-MIP/SiO2 NPs were coated on the thin layer chromatography plate, and coupled with miniaturized fluorimeter for fluorescence detection of MG in fish samples. The resulting CdTe-MIP/SiO2 NPs based system possessed good linearity (0.01 ∼ 20 μmol/L), high recoveries (98.36 %∼101.45 %) and low detection limit (3.7 nmol/L) for MG. Furthermore, CdTe-MIP/SiO2 NPs based system were employed to measure fish samples spiked with MG, meanwhile, HPLC was utilized to evaluate the accuracy and reliability. And the paired t-test was conducted to evaluate differences between fluorescence method and HPLC, P > 0.05 means no significant difference was observed, the results demonstrated that both fluorescence method and HPLC are suitable for MG analysis.

High mobility group box 1 mediates inflammatory responses in malignant peritoneal mesothelioma

BACKGROUND

Serum high mobility group box 1 (HMGB1) serves as a diagnostic biomarker for malignant peritoneal mesothelioma (MPM) patients, yet its diagnostic significance within MPM tumor tissues remains uncertain. This study aims to elucidate the roles of HMGB1 in MPM.

METHODS

HMGB1 expression analysis was conducted in both tumor and adjacent non-cancerous tissues collected from MPM patients. The two-year follow-up of MPM patients commenced from the diagnosis date. Inflammatory cytokine analysis was performed on these tissues, and Pearson correlation coefficient analysis was applied to examine variable relationships. In vitro assays included constructing an HMGB1 knockdown cell line, assessing cell viability, apoptosis, and inflammatory cytokine levels to delineate HMGB1's roles in MPM.

RESULTS

HMGB1 overexpression was observed in MPM tumor tissues, particularly in stages III-IV. Diagnostic implications of HMGB1 for MPM were evident, augmenting its diagnostic value. HMGB1 overexpression correlated with diminished survival rates. Positive correlations existed between inflammatory cytokines and HMGB1 in MPM tumor tissues and cell lines. Suppression of HMGB1 regulated cell growth and apoptosis in MPM cell lines.

CONCLUSION

HMGB1 exhibits diagnostic potential for MPM and modulates inflammatory responses within the disease context.

mRNA-LNP HIV-1 trimer boosters elicit precursors to broad neutralizing antibodies

Germline-targeting (GT) HIV vaccine strategies are predicated on deriving broadly neutralizing antibodies (bnAbs) through multiple boost immunogens. However, as the recruitment of memory B cells (MBCs) to germinal centers (GCs) is inefficient and may be derailed by serum antibody-induced epitope masking, driving further B cell receptor (BCR) modification in GC-experienced B cells after boosting poses a challenge. Using humanized immunoglobulin knockin mice, we found that GT protein trimer immunogen N332-GT5 could prime inferred-germline precursors to the V3-glycan-targeted bnAb BG18 and that B cells primed by N332-GT5 were effectively boosted by either of two novel protein immunogens designed to have minimum cross-reactivity with the off-target V1-binding responses. The delivery of the prime and boost immunogens as messenger RNA lipid nanoparticles (mRNA-LNPs) generated long-lasting GCs, somatic hypermutation, and affinity maturation and may be an effective tool in HIV vaccine development.

mRNA-LNP prime boost evolves precursors toward VRC01-like broadly neutralizing antibodies in preclinical humanized mouse models

Germline-targeting (GT) protein immunogens to induce VRC01-class broadly neutralizing antibodies (bnAbs) to the CD4-binding site of the HIV envelope (Env) have shown promise in clinical trials. Here, we preclinically validated a lipid nanoparticle-encapsulated nucleoside mRNA (mRNA-LNP) encoding eOD-GT8 60mer as a soluble self-assembling nanoparticle in mouse models. In a model with three humanized B cell lineages bearing distinct VRC01-precursor B cell receptors (BCRs) with similar affinities for eOD-GT8, all lineages could be simultaneously primed and undergo diversification and affinity maturation without exclusionary competition. Boosts drove precursor B cell participation in germinal centers; the accumulation of somatic hypermutations, including in key VRC01-class positions; and affinity maturation to boost and native-like antigens in two of the three precursor lineages. We have preclinically validated a prime-boost regimen of soluble self-assembling nanoparticles encoded by mRNA-LNP, demonstrating that multiple lineages can be primed, boosted, and diversified along the bnAb pathway.

Recurrent patellar dislocation patients with high-grade J-sign have multiple structural bone abnormalities in the lower limbs

PURPOSE

To explore the relationship between preoperative J-sign grading and structural bone abnormalities in patients with recurrent patellar dislocation (RPD).

METHODS

A retrospective study was conducted on RPD patients over 5 years. Patients were categorised based on J-sign grade into low (J- and J1+), moderate (J2+) and high groups (J3+). Trochlear dysplasia (TD) and osseous structures (femoral anteversion angle [FAA], knee torsion, tibial tuberosity-trochlear groove [TT-TG] distance, Caton-Deschamps index) were assessed and grouped according to risk factor thresholds. The χ2 test was used to compare composition ratio differences of structural bone abnormalities among the groups.

RESULTS

A total of 256 patients were included, with 206 (80.5%) females. The distribution of J-sign grade was as follows: 89 knees (34.8%) of low grade, 86 moderate (33.6%) and 81 high (31.6%). Among the five structural bone abnormalities, TD was the most common with a prevalence of 78.5%, followed by increased TT-TG at 47.4%. Excessive tibiofemoral rotation had the lowest occurrence at 28.9%. There were 173 (67.6%) patients who had two or more abnormalities, while 45 (17.6%) had four to five bony abnormalities. Among patients with any bony abnormality, the proportion of high-grade J-sign surpassed 40%. Patients with moderate and high-grade J-sign had more increased FAA and more pronounced patella alta (all p < 0.001). The proportion of excessive knee torsion and TD increased with increasing each J-sign grade, with the more notable tendency in knee torsion (high vs. moderate vs. low-grade: 61% vs. 22% vs 7%, p < 0.001). Furthermore, the higher J-sign grade was also associated with more combined bony abnormalities (p < 0.001). In the high-grade J-sign group, 90.2% of the knees had two or more bony risk factors and 40.7% had four or more, which were significantly higher than the moderate and low-grade J-sign groups (40.7% vs. 11.6% vs. 2.2%, p < 0.001).

CONCLUSION

In patients with a high-grade J-sign, over 90% of the lower limbs had two or more structural bone risk factors, and more than 40% had four or more. These proportions were significantly higher compared to knees with low-grade and moderate J-sign. In clinical practice, when treating high-grade patellar mal-tracking, it is important to focus on and correct these strongly correlated abnormal bone structures.

LEVEL OF EVIDENCE

Level III.

Design, Synthesis, Antifungal Activity, and Molecular Docking Studies of Novel Chiral Isoxazoline-Benzofuran-Sulfonamide Derivatives

Succinate dehydrogenase (SDH) is one of the most important molecular targets for the development of novel fungicides. With the emerging problem of resistance in plant fungal pathogens, novel compounds with high fungicidal activity need to be developed, but the study of chiral pesticides for the inhibition of highly destructive plant pathogens has been rarely reported in recent years. Therefore, a series of novel chiral isoxazoline-benzofuran-sulfonamide derivatives were designed to investigate potential novel antifungal molecules. The chiral target compound 3a was cultured as a single crystal and confirmed using X-ray diffraction. All the target compounds were tested for antifungal activity, and compounds 3c, 3i, 3s, and 3r were found to have significant antifungal effects against S. sclerotiorum with EC50 values of 0.42 mg/L, 0.33 mg/L, 0.37 mg/L, and 0.40 mg/L, respectively, which were superior to the commercial fungicide fluopyram (EC50 = 0.47 mg/L). The IC50 value of compound 3i against the SDH of S. sclerotiorum was 0.63 mg/mL, which was further demonstrated by enzyme activity assays. Scanning electron microscopy showed that 3i had a significant inhibitory effect on S. sclerotiorum. In addition, the fluorescence quenching analysis assay indicated that compound 3i had a similar effect with the positive control fluopyram. Molecular docking exhibited that target compounds with chiral configuration had better affinity than racemic configuration, and 3i possessed stronger action than fluopyram, which was in keeping with the in vitro test results. These results would provide a basis and reference for the development of novel chiral fungicides.

Effect of dietary supplementation Ampelopsis grossedentata extract on growth performance and muscle nutrition of Megalobrama hoffmanni by gut bacterial mediation

Nowadays, Megalobrama hoffmanni is a typical cultured fish in south China due to its resource decline in the Pearl River. Meanwhile, since antibiotics had been banned internationally, Chinese medical herbal plant serving as alternative to antibiotics has been adopted in aquaculture. In the present study, to ensure the health growth of M. hoffmanni, extract of traditional medical herbal plant Ampelopsis grossedentata was dietary supplemented and a series experiments were performed, including growth performance determination, physiological/biochemical detection, nutrition analysis, histology analysis, and 16S rRNA amplicon sequencing. Growth performance enhancement was determined since the weight gain rate (WGR), specific growth rate (SGR), and condition factor (CF) of M. hoffmanni increased as feeding inclusion A. grossedentata extract. Interestingly, the total content of muscle fatty acids ascended via supplementing A. grossedentata extract at middle level, in which group the activities of superoxide dismutase (SOD) and catalase (CAT) significantly increased and thus retarded the lipid peroxidation process (manifesting as malondialdehyde (MDA) content rising). Additionally, immune response and inflammatory reaction was stimulated in low and high level A. grossedentata extract added groups, indicating a suitable dosage of A. grossedentata extract benefited in safety production. Moreover, gut microbiota community varied hugely as daily supplementation A. grossedentata extract and the keystone species were tightly related to lipid transformation, which ultimately led to fatty acids composition variation. Our results confirmed that dietary supplementation A. grossedentata extract at the middle level (0.5‰, w/w) is suitable for serving as feed additive in healthful aquaculture of M. hoffmanni.

Cytokine Storm Related to CD4+ T Cells in Influenza Virus-Associated Acute Necrotizing Encephalopathy

Acute necrotizing encephalopathy (ANE) is a rare but deadly complication with an unclear pathogenesis. We aimed to elucidate the immune characteristics of H1N1 influenza virus-associated ANE (IANE) and provide a potential therapeutic approach for IANE. Seven pediatric cases from a concentrated outbreak of H1N1 influenza were included in this study. The patients' CD4+ T cells from peripheral blood decreased sharply in number but highly expressed Eomesodermin (Eomes), CD69 and PD-1, companied with extremely high levels of IL-6, IL-8 in the cerebrospinal fluid and plasma. Patient 2, who showed high fever and seizures and was admitted to the hospital very early in the disease course, received intravenous tocilizumab and subsequently showed a reduction in temperature and a stable conscious state 24 h later. In conclusion, a proinflammatory cytokine storm associated with activated CD4+ T cells may cause severe brain pathology in IANE. Tocilizumab may be helpful in treating IANE.

ASSESSING THE CAUSAL RELATIONSHIP BETWEEN SEPSIS AND AUTOIMMUNE: A MENDELIAN RANDOMIZATION STUDY

ABSTRACT

Objective : Numerous epidemiological studies have identified a potential link between sepsis and a variety of autoimmune disorders. The primary objective of this study is to delve deeper into this connection, investigating the potential causal relationship between sepsis and autoimmune disorders through the application of Mendelian randomization (MR). Methods : To assess the potential genetic impact on sepsis risk relating to susceptibility toward immune-related outcomes, we used summary data from the largest European genome-wide association studies (GWAS) on these conditions using a two-sample MR framework. Single nucleotide polymorphisms-which had strong associations with the nine traits-were extracted from the GWAS and examined their effects in an extensive European sepsis GWAS (486,484 cases and 474,841 controls). We used inverse-variance weighted MR, weighted median, and MR Egger for analyses, supplementing these with sensitivity analyses and assessing level pleiotropy using MR methodologies. We also executed a reverse MR analysis to test sepsis' causal effects on the designated autoimmune traits. Results : With primary sclerosing cholangitis being the exception, our MR analysis suggests that susceptibility toward most autoimmune diseases does not affect sepsis risks. The reverse MR analysis did not validate any influence of sepsis susceptibility over other autoimmune diseases. Our primary inverse-variance weighted MR analysis outcomes found general confirmation through our sensitivity MR examinations. Variance in the exposures, as dictated by the single nucleotide polymorphism sets used as MR instruments, ranged between 4.88 × 10 -5 to 0.005. Conclusion : Our MR research, centered on a European population, does not validate a correlation between susceptibility to the majority of autoimmune disorders and sepsis risk. Associations discerned in epidemiological studies may owe partly to shared biological or environmental confounders. The risk susceptibility for primary sclerosing cholangitis does relate to sepsis risk, opening doors for personalized precision treatments in the future.

Optimization of Forward Collision Warning Algorithm Considering Truck Driver Response Behavior Characteristics

Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers' responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms' minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify driver response behavior into three categories: Response Before Warning (RBW), Response After Warning (RAW), and No Response (NR). Results showed that RBW clusters tend to occur at long following distances (>19 m), and drivers applied braking before the warning. In RAW clusters, deceleration after warning is significantly more forceful than before warning. NR clusters occur at short distances, and deceleration fluctuates only slightly. To optimize the FCW algorithm, the warning distance was divided into reaction distance and braking distance. The linear support vector machine was used to fit the driver reaction distance. The long short-term memory method was used to predict braking distance based on each of the three response scenarios: R2 was 0.896 for RAW scenarios, 0.927 for RBW scenarios, and 0.980 for NR scenarios. Verification results show that the optimized truck FCW algorithm improved safety by 1 % to 5.1 %; accuracy reached 97.92 %, and the false alarm rate was 1.73 %.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

Determination of aloesone in rat plasma by LC-MS/MS spectrometry and its application in a pharmacokinetic study

Aim: We aimed to develop a rapid and accurate LC-MS/MS method for determining the concentration of aloesone in rat plasma, and to investigate its pharmacokinetics. Methods: The rat plasma samples were extracted using acetonitrile. Chromatographic separation was achieved using a Kinetex XB-C18 column, with a mobile phase of methanol and water (containing 0.1‰ formic acid) in a gradient elution. An ESI source, operating in positive ion mode with multiple reaction monitoring, was utilized. Results & conclusion: The developed method meets all the requirements for methodological validation, and it was successfully applied in the pharmacokinetic study. It was observed that oral administration of aloesone in rats resulted in rapid absorption (time to reach Cmax: 0.083 h) but low bioavailability (12.59%).

Nitroreductase-Responsive Photosensitizers for Selective Imaging and Photo-Inactivation of Intracellular Bacteria

Intracellular Staphylococcus aureus (S. aureus), especially the methicillin resistant staphylococcus aureus (MRSA), are difficult to detect and eradicate due to the protection by the host cells. Antibacterial photodynamic therapy (aPDT) offers promise in treating intracellular bacteria, provided that selective damage to the bacteria ranther than host cells can be realized. According to the different nitroreductase (NTR) levels in mammalian cells and S. aureus, herein NTR-responsive photosensitizers (PSs) (T)CyI-NO2 were designed and synthesized. The emission and 1O2 generation of (T)CyI-NO2 are quenched by the 4-nitrobenzyl group, but can be specifically switched on by bacterial NTR. Therefore, selective imaging and photo-inactivation of intracellular S. aureus and MRSA were achieved. Our findings may pave the way for the development of more efficient and selective aPDT agents to combat intractable intracellular infections.

Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns

Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.

Sexual and Reproductive Health Outcomes Among Adolescent Females During the COVID-19 Pandemic

BACKGROUND AND OBJECTIVES

Coronavirus disease 2019 (COVID-19) posed a significant threat to adolescents' sexual and reproductive health. In this study, we examined population-level pregnancy and sexual health-related care utilization among adolescent females in Ontario, Canada during the pandemic and evaluated relationships between these outcomes and key sociodemographic characteristics.

METHODS

This was a population-based, repeated cross-sectional study of >630 000 female adolescents (12-19 years) during the prepandemic (January 1, 2018-February 29, 2020) and COVID-19 pandemic (March 1, 2020-December 31, 2022) periods. Primary outcome was pregnancy; secondary outcomes were contraceptive management visits, contraception prescription uptake, and sexually transmitted infection (STI) management visits. Poisson models with generalized estimating equations for clustered count data were used to model pre-COVID-19 trends and forecast expected rates during the COVID-19 period. Absolute rate differences between observed and expected outcome rates for each pandemic month were calculated overall and by urbanicity, neighborhood income, immigration status, and region.

RESULTS

During the pandemic, lower-than-expected population-level rates of adolescent pregnancy (rate ratio 0.87; 95% confidence interval [CI]:0.85-0.88), and encounters for contraceptive (rate ratio 0.82; 95% CI:0.77-0.88) and STI management (rate ratio 0.52; 95% CI:0.51-0.53) were observed. Encounter rates did not return to pre-pandemic rates by study period end, despite health system reopening. Pregnancy rates among adolescent subpopulations with the highest pre-pandemic pregnancy rates changed least during the pandemic.

CONCLUSIONS

Population-level rates of adolescent pregnancy and sexual health-related care utilization were lower than expected during the COVID-19 pandemic, and below-expected care utilization rates persist. Pregnancy rates among more structurally vulnerable adolescents demonstrated less decline, suggesting exacerbation of preexisting inequities.

Dual Parallel Policy Iteration With Coupled Policy Improvement

In this article, a novel coupled policy improvement mechanism is developed for improving policy iteration (PI) algorithms. In contrast to the common PI, the developed dual parallel policy iteration (DPPI) with coupled policy improvement mechanism consists of two parallel PIs. At each PI step, the performances of the two parallel policies are evaluated and the better one is defined as the dominant policy. Then, the dominant policy is used to guide the parallel policy improvement in a soft manner by constraining the Kullback-Liebler (KL) divergence between the dominant policy and the policy to be updated. It is proven that the convergence of DPPI can be guaranteed under the designed coupled policy improvement mechanism. Moreover, it is clearly shown that under certain conditions, the Q -functions of the two new policies obtained in each parallel policy improvement are larger than those of all the previous dominant policies, which is conductive to accelerate the PI process and improve the policy learning efficiency to some extent. Furthermore, by combining DPPI with the twin delay deep deterministic (TD3) policy gradient, we propose a reinforcement learning (RL) algorithm: parallel TD3 (PTD3). Experimental results on continuous-action control tasks in the MuJoCo and OpenAI Gym platforms show that the proposed PTD3 outperforms the state-of-the-art RL algorithms.

Differences in maternal-newborn ABO blood groups and risk of serious infant infection

BACKGROUND

During pregnancy, various maternal IgG antibodies are transferred to the developing fetus, some of which may protect the newborn against infection. If a mother and her fetus have different ABO blood groups, then transferred maternal antibodies may plausibly protect the infant against infection.

AIM

To determine if maternal-newborn ABO blood group incongruence vs. congruence is associated with a lower risk of serious infection in the infant.

DESIGN

Retrospective population-based cohort.

METHODS

We used linked patient-level datasets for all singleton hospital livebirths from 2008-2022 in Ontario, Canada, with known maternal and newborn ABO blood groups. We used a dichotomous exposure state, either ABO blood group congruent (N = 114,507) or incongruent (N = 43,074). The main outcome of interest was the risk of serious infant infection within 27 days, and from 28-365 days, after birth. Cox proportional hazard models generated hazard ratios and 95% confidence intervals, and were adjusted for maternal age, world region of origin, residential income quintile, and gestational age at birth.

RESULTS

Relative to maternal-newborn congruency, incongruent ABO blood group was associated with aHR of 0.88 (95% CI 0.80 to 0.97) for serious neonatal infection within 27 days of birth, and 0.93 (95% CI 0.90 to 0.96) for serious infection between 28-365 days after birth.

CONCLUSIONS

Maternal-newborn ABO incongruence may be associated with a lower relative risk of a serious infant infection within 27 days, and from 28 to 365 days, after birth.

A photo-degradable BODIPY-modified Ru(II) photosensitizer for safe and efficient PDT under both normoxic and hypoxic conditions

Photodynamic therapy (PDT) is promising for cancer treatment but still suffers from some limitations. For instance, PDT based on 1O2 generation (in a type-II mechanism) is heavily dependent on high oxygen concentrations and will be significantly depressed in hypoxic tumors. In addition, the residual photosensitizers after PDT treatment may cause severe side-effects under light irradiation. To solve these problems, herein a BODIPY (boron dipyrromethene)-modified Ru(II) complex [Ru(dip)2(tpy-BODIPY)]2+ (complex 1, dip = 4,7-diphenyl-1,10-phenanthroline, tpy = 2,2':6',2''-terpyridine) was designed and synthesized. Complex 1 exhibited both high singlet oxygen quantum yield (Φ = 0.7 in CH3CN) and excellent superoxide radical (O2˙-) generation, and thus demonstrated efficient PDT activity under both normoxic and hypoxic conditions. Moreover, complex 1 is photo-degradable in water, and greatly loses its ROS generation ability after PDT treatment. These novel properties of complex 1 make it promising for efficient PDT under both normoxic and hypoxic conditions with reduced side-effects.

Effectiveness and safety of auricular acupuncture on adjuvant analgesia in patients with total knee arthroplasty: a randomized sham-controlled trial

Objective

This study aimed to evaluate the effectiveness and safety of auricular acupuncture (AA) on postoperative analgesia, the degree of postoperative nausea, and the effect of inflammation after total knee arthroplasty (TKA).

Methods

This was a single-center, placebo-controlled, randomized clinical trial. In total, 96 patients were randomly divided into an AA group with an indwelling intradermal needle (n = 48) and a sham auricular acupuncture (SAA) group with a non-penetrating placebo needle (n = 48). Intra-spinal anesthesia was adopted in both groups during surgery, and an epidural analgesic pump was implanted after surgery for 48 h. The primary outcome was the post-surgery visual analog score (VAS) of resting and movement states (at 6, 12 h and 1, 2, 3, 5, and 7 days). The secondary outcomes included additional doses of analgesic injection during the treatment, C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), and white blood cell (WBC) count on the 1st, 3rd, and 7th day after the operation, nausea on the 1st, 2nd, and 3rd day after the operation, the Hospital for Special Surgery Knee Score (HSS) on the 2nd and 12th week after the operation, and adverse events.

Results

The VAS in the AA group at 6 h, 12 h, 2, 3, and 5 days after surgery were lower than those of the SAA group (p < 0.05). Among the secondary outcomes, the total dose of additional analgesic injection after surgery in the AA group was lower than that in the SAA group (p < 0.05). The serum CRP on the 1st day after operation in the AA group was lower than that in the SAA group (p < 0.05). The degree of nausea on 2nd day after surgery in the AA group was lower than that in the SAA group (p < 0.05). There was no significant difference in other outcomes (p > 0.05).

Conclusion

In this study, AA was shown to be an effective and safe complementary and alternative therapy for pain relief after TKA, which was able to reduce the total postoperative dose of additional painkillers, decrease serum CRP 1 day after surgery, and improve the degree of postoperative nausea.

Clinical trial registration

www.chictr.org.cn, ChiCTR2100054403.

Total alditols from Cistanche deserticola attenuate functional constipation by regulating bile acid metabolism

HEADINGS ETHNOPHARMACOLOGICAL RELEVANCE

Functional constipation (FC), characterized by chronic constipation, significantly impacts physiological function and induces psychological stress in patients. However, current clinical treatment options for FC are currently limited. Cistanche deserticola, a traditional Chinese medicine that promotes intestinal moisture and bowel relaxation, contains cistanche total alditol extract (CTAE) as its primary active extract. However, the production of CTAE, its overall efficacy, and potential mechanisms for treating FC have yet to been investigated.

AIM OF THE STUDY

This study aimed to reveal the overall efficacy and potential mechanism of action of CTAE in rats with FC using a combination of stable preparation, pharmacodynamics, non-targeted metabolomics, bile acid metabolomics, and western blotting.

MATERIALS AND METHODS

Fourteen batches of CTAE underwent quality testing. A rat model of FC was developed using diphenoxylate tablets. The comprehensive pharmacodynamic effects of CTAE on FC were evaluated using fecal characteristics (wet weight, dry weight, and water content), intestinal transmission (colonic EMG amplitude, colonic EMG frequency, propulsion length, and propulsion rate), serum and colon biochemical indicators, distribution of interstitial cells of Cajal (ICC), and pathological examination. Non-targeted metabolomics was performed to assess the changes in endogenous metabolite profiles induced by CTAE. Bile acid metabolomics and western blotting analyses were employed to validate the potential mechanisms of action of CTAE.

RESULTS

CTAE, with a total content of betaine, mannitol, D-fructose, glucose, and sucrose of (75.67 ± 3.73) %, significantly enhanced intestinal transit, regulated neurotransmitters, increased the expression of c-kit in ICC, and alleviated intestinal inflammation in rats with FC. Non-targeted metabolomics revealed that CTAE significantly alleviated FC-induced metabolic disorders, mainly the biosynthesis of primary bile acids. Targeted metabolomic analysis confirmed that CTAE regulated FC-induced bile acid disorders. Western-blotting results confirmed that CTAE increased the expression of CYP8B1, FGF15, TGR5, and FXR, thereby modulating bile acid synthesis and enterohepatic circulation.

CONCLUSION

CTAE demonstrates significant therapeutic effects on FC, primarily through the regulation of bile acid synthesis and enterohepatic circulation. These findings provide a promising foundation for the development and clinical application of novel CATE-based drugs.

Introducing gradient Er ions and oxygen defects into SrCoO3 for regulating structural, electrical and magnetic transport properties

The SrCoO3-δ system has broad application potential due to its diverse crystal structures, oxidation stoichiometric ratio, and significant electrical and magnetic properties. However, it faces the challenges of a complex crystal structure and oxygen defect control in this material system. Herein, we introduce oxygen defects into SrCoO3-δvia Er doping to regulate the structural, electrical and magnetic transport properties. Sr1-xErxCoO3-δ (x = 0-0.25) undergoes an evolution of structure and oxygen content (measured using the iodometric method) from hexagonal SrCoO2.626 (H + Co3O4) to cubic perovskite Sr0.9Er0.1CoO2.689 (CP) and finally to ordered tetragonal Sr0.8Er0.2CoO2.635 (OT). Among the three phases, Sr0.9Er0.1CoO2.689 (CP) exhibits the lowest resistivity, only 4.06 mΩ cm at room temperature, which is attributed to its high three-dimensional symmetry, overlap of O 2p and Co 3d orbitals at high oxygen ion concentration. Further introduction of Er ions and oxygen defects promotes the transformation from low spin Co4+ (LS, t52ge0g, S = 1/2) to high spin Co3+ (HS, t42ge2g, S = 2), and from the CoO6 octahedron (low magnetic moment transformation) to the CoO4.25 tetrahedron (high magnetic moment). The oxygen-deficient CoO4.25 layer appears, which can enhance the ordering of A sites and oxygen vacancies, and the CP phase transforms into room-temperature ferromagnetic Sr0.8Er0.2CoO2.635 (OT, TC∼330 K). Er ions provide unpaired electrons in the 2f orbital, which results in a strong magnetization of Sr0.8Er0.2CoO2.635 (OT, 4.66 μB/Co) at low temperatures.

Association of a calcium channel blocker and diuretic prescribing cascade with adverse events: A population-based cohort study

BACKGROUND

Prescribing cascades occur when a drug adverse event is misinterpreted as a new medical condition and a second, potentially unnecessary drug, is prescribed to treat the adverse event. The population-level consequences of prescribing cascades remain unknown.

METHODS

This population-based cohort study used linked health administrative databases in Ontario, Canada. The study included community-dwelling adults, 66 years of age or older with hypertension and no history of heart failure (HF) or diuretic use in the prior year, newly dispensed a calcium channel blocker (CCB). Individuals subsequently dispensed a diuretic within 90 days of incident CCB dispensing were classified as the prescribing cascade group, and compared to those not dispensed a diuretic, classified as the non-prescribing cascade group. Those with and without a prescribing cascade were matched one-to-one on the propensity score and sex. The primary outcome was a serious adverse event (SAE), which was the composite of emergency room visits and hospitalizations in the 90-day follow-up period. We estimated hazard ratios (HRs) with 95% confidence intervals (CI) for SAE using an Andersen-Gill recurrent events regression model.

RESULTS

Among 39,347 older adults with hypertension and no history of HF who were newly dispensed a CCB, 1881 (4.8%) had a new diuretic dispensed within 90 days after CCB initiation. Compared to the non-prescribing cascade group, those in the prescribing cascade group had higher rates of SAEs (HR: 1.21, 95% CI: 1.02-1.43).

CONCLUSIONS

The CCB-diuretic prescribing cascade was associated with an increased rate of SAEs, suggesting harm beyond prescribing a second drug therapy. Our study raises awareness of the downstream impact of the CCB-diuretic prescribing cascade at a population level and provides an opportunity for clinicians who identify this prescribing cascade to review their patients' medications to determine if they can be optimized.

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers' own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle-pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

Effects of insomnia on risky driving behavior among bus drivers: The mediating effect of mental health

Crashes caused by problems with bus drivers' physical and mental health have increased in frequency in recent years. Insomnia, a common type of sleep problem, has significant positive relationships with both crash risk and mental health problems, especially anxiety and depression, which are themselves associated with driving behavior. However, few studies have conducted analysis on sleep-related problems and mental health exclusively on bus drivers, nor on how these problems influence driving performance. Thus, this study explored the effect of insomnia and mental health on bus drivers' risky driving behavior and evaluated the interaction of four variables: insomnia, anxiety, depression, and risky driving behavior. The survey-based investigation was conducted in a bus company in Suzhou, China, with 1,295 bus drivers participating. Insomnia, anxiety, and depression were self-reported based on professional mental health scales and risky driving behaviors were measured by the Driver Behavior Questionnaire. Two mediation models and a chain mediation model were developed to examine relationships among the bus drivers' insomnia, anxiety, depression, and risky driving behavior. Results revealed that (a) bus drivers less than 31 years old, drivers with more than 11 years' experience driving buses, and those with crash and violation involvement within three years demonstrated more severe degrees of insomnia, anxiety, depression, and risky driving behavior; (b) there were significant positive correlations and interactions among the four variables. Results specifically related to the interaction among variables include findings that (a) anxiety mediated between insomnia and risky driving behavior; (b) depression mediated between insomnia and risky driving behavior; and (c) anxiety affected bus drivers' risky driving behavior primarily though depression. The findings in this study indicate the importance of regular physical and mental health examination of bus drivers and suggest that interventions focused on insomnia and mental health problems may be helpful to reduce risky driving behaviors of bus drivers both directly and indirectly.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

A case of refractory generalized pustular psoriasis treated with upadacitinib

Here we report a case of upadacitinib in the treatment of generalized pustular psoriasis complicated with psoriatic arthritis. We chose upadacitinib after the use of acitretin did not work well, and achieved a tremendous therapeutic effect. Upadacitinib not only relieved the joint symptoms and fever, but also played a good role in the regression of pustules. At present, there are no successful cases of Janus kinase inhibitor use in the treatment of generalized pustular psoriasis.

Non-alcoholic fatty liver disease promotes liver metastasis of colorectal cancer via fatty acid synthase dependent EGFR palmitoylation

Liver metastasis is the major reason for most of colorectal cancer (CRC) related deaths. Accumulating evidence indicates that CRC patients with non-alcoholic fatty liver disease (NAFLD) are at a greater risk of developing liver metastasis. With the growing prevalence of NAFLD, a better understanding of the molecular mechanism in NAFLD-driven CRC liver metastasis is needed. In this study, we demonstrated that NAFLD facilitated CRC liver metastasis as a metabolic disorder and promoted the stemness of metastatic CRC cells for their colonization and outgrowth in hepatic niches. Metabolically, the lipid-rich microenvironment in NAFLD activated de novo palmitate biosynthesis in metastatic CRC cells via upregulating fatty acid synthase (FASN). Moreover, increased intracellular palmitate bioavailability promoted EGFR palmitoylation to enhance its protein stability and plasma membrane localization. Furthermore, we demonstrated that the FDA-approved FASN inhibitor orlistat could reduce NAFLD-activated endogenous palmitate production, thus inhibiting palmitoylation of EGFR to suppress CRC cell stemness and restrict liver metastasis in synergy with conventional chemotherapy. These findings reveal that the NAFLD metabolic microenvironment boosts endogenous palmitate biosynthesis in metastatic CRC cells and promotes cell stemness via EGFR palmitoylation, and FASN inhibitor orlistat could be a candidate adjuvant drug to suppress liver metastasis in CRC patients with NAFLD.

A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making

Clustering is an effective means to reduce the scaling of large-scale group decision-making (LSGDM). However, there are many problems with clustering methods, such as incomplete or ambiguous information usually provided by different decision makers. Traditional clustering methods may not be able to handle these situations effectively, resulting in incomplete decision-making information. Calculating the clustering centers may become very complex and time-consuming. Inappropriate distance weights may also lead to incorrect cluster assignments, and these problems will seriously affect the clustering results. This research provides a novel incomplete hesitant fuzzy information supplement and clustering approach for large-scale group decision-making in order to address the aforementioned difficulties. First, the approach takes into account the trust degradation and the inhibition of relationships of distrust in the process of trust propagation, and then it builds a global and local network of trust. A novel supplemental formula is provided that takes into account the decision-preference maker's as well as the trust-neighbor's information, allowing the decision-neighbor maker's recommendation to be realized. Therefore, an improved distance function can be proposed to calculate the weights by combining the relative standard deviation theory and selecting the selected clustering centers by using the density peaks in order to optimize the selection of clustering centers and reduce the complexity and scaling of the decision. Finally, an example is presented to demonstrate how the proposed method can be applied. The consistency index and comparison experiments are used to evaluate if the suggested approach is effective and reliable.

Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.

Antibody production relies on the tRNA inosine wobble modification to meet biased codon demand

Antibodies are produced at high rates to provide immunoprotection, which puts pressure on the B cell translational machinery. Here, we identified a pattern of codon usage conserved across antibody genes. One feature thereof is the hyperutilization of codons that lack genome-encoded Watson-Crick transfer RNAs (tRNAs), instead relying on the posttranscriptional tRNA modification inosine (I34), which expands the decoding capacity of specific tRNAs through wobbling. Antibody-secreting cells had increased I34 levels and were more reliant on I34 for protein production than naïve B cells. Furthermore, antibody I34-dependent codon usage may influence B cell passage through regulatory checkpoints. Our work elucidates the interface between the tRNA pool and protein production in the immune system and has implications for the design and selection of antibodies for vaccines and therapeutics.

Compact Broad Learning System Based on Fused Lasso and Smooth Lasso

Aiming at simplifying the network structure of broad learning system (BLS), this article proposes a novel simplification method called compact BLS (CBLS). Groups of nodes play an important role in the modeling process of BLS, and it means that there may be a correlation between nodes. The proposed CBLS not only focuses on the compactness of network structure but also pays closer attention to the correlation between nodes. Learning from the idea of Fused Lasso and Smooth Lasso, it uses the L1 -regularization term and the fusion term to penalize each output weight and the difference between adjacent output weights, respectively. The L1 -regularization term determines the correlation between the nodes and the outputs, whereas the fusion term captures the correlation between nodes. By optimizing the output weights iteratively, the correlation between the nodes and the outputs and the correlation between nodes are attempted to be considered in the simplification process simultaneously. Without reducing the prediction accuracy, finally, the network structure is simplified more reasonably and a sparse and smooth output weights solution is provided, which can reflect the characteristic of group learning of BLS. Furthermore, according to the fusion terms used in Fused Lasso and Smooth Lasso, two different simplification strategies are developed and compared. Multiple experiments based on public datasets are used to demonstrate the feasibility and effectiveness of the proposed methods.

A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations

During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors' effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement.

Molecular characteristics of carbapenem-resistant Raoultella ornithinolytica

Aim of this study was to explore molecular characteristics and resistance mechanisms of carbapenem-resistant Raoultella ornithinolytica (CR-ROR) isolated from patients in a hospital in China. Three CR-ROR strains were collected and bacterial identification was done by Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) Vitek-MS and by digital DDH analysis. VITEK 2 compact system and Kirby-Bauer (K-B) disk diffusion were used for antimicrobial susceptibility testing. Whole genome sequencing was carried out using the Illumina platform NovaSeq sequencer. Abricate software was used for the prediction of antibiotic resistance genes of three CR-ROR strains. The phylogenetic tree was constructed through genome SNPs to investigate the genetic relationship of three CR-ROR strains. Three CR-ROR (WF1357, WF2441, and WF3367) strains were collected in this study. Two strains were isolated from neurosurgery (WF1357 and WF2441), and one was isolated from pulmonology department (WF3367). All strains harboured multiple antibiotic resistance genes. Two strains (WF1357, WF2441) carried the blaNDM-1 gene, one of the strains (WF3367) carried the blaKPC-2 gene. Three CR-RORs were resistant to different antimicrobial agents including carbapenems. The three CR-ROR strains collected in this study and 51 CR-ROR strain genomes downloaded from NCBI, were divided into six evolutionary groups (A-F). In this study, three CR-ROR strains were found to have a higher level of resistance to antibacterial agents and carried multiple antibiotic resistance genes. The CR-ROR strains carrying multiple antibacterial resistant genes require the stringent monitoring to avoid the spread of multidrug-resistant bacterial strains.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Knowledge mapping of acupoint sensitization and acupoint specificity: a bibliometric analysis

Objective

The relationship between acupoint sensitization and acupoint specificity is a topic of significant interest in acupuncture research. Numerous clinical studies have demonstrated that needling pain sensitive acupoints yields superior therapeutic outcomes compared to traditional acupoints, particularly in the context of pain disorders. However, there is a lack of bibliometric analysis in acupuncture area. Therefore, the objective of this study is to offer a comprehensive overview of the knowledge structure and research hotspots pertaining to acupoint sensitization and acupoint specificity.

Methods

The search for publications pertaining to acupoint sensitization and acupoint specificity was conducted in the Web of Science Core Collection (WoSCC) database from its inception until August 11, 2023. Subsequently, bibliometric analyses were carried out using VOSviewer, CiteSpace, R software (Bibliometrix package), and GraphPad Prism software.

Results

This study includes 4,940 articles from 72 countries, with China and the United States being the leading countries. The number of publications related to acupoint sensitization and specificity has been increasing annually. Major research institutions involved in this field include the Shanghai University of Traditional Chinese Medicine, Kyung Hee University, Beijing University of Chinese Medicine, Chinese Academy of Chinese Medical Sciences, and China Medical University, among others. "Evidence-based Complementary and Alternative Medicine" is the most popular journal in acupuncture field, and "PAIN" is the most co-cited journal. Publications are contributed by 20,325 authors from all over the world, with Wu Huangan, Fang Jianqiao, Lin Yi-Wen, Liu Huirong, and Chen Rixin having published the most articles. Han Ji-Sheng is the most cited author in this research area. The main directions include the study of temperature specificity of acupoints, the diagnosis of acupoint sensitization diseases, and the study of the mechanism of acupoint sensitization. The most listed keywords in recent years are "TRPV1," "signaling pathway," and "diagnosis."

Conclusion

This is the first bibliometric study to comprehensively summarize research trends and advances in acupoint sensitization and acupoint specificity, and the information highlights recent research preliminary and main directions that serve as a reference for acupoint sensitization and acupoint specificity research.

Prescribed Performance Tracking Control Under Uncertain Initial Conditions: A Neuroadaptive Output Feedback Approach

This work is concerned with the prescribed performance tracking control for a family of nonlinear nontriangular structure systems under uncertain initial conditions and partial measurable states. By combining neural network and variable separation technique, a state observer with a simple structure is constructed for output-based finite-time tracking control, wherein the issue of algebraic loop arising from a nontriangular structure is circumvented. Meanwhile, by using an error transformation, the developed control scheme is able to ensure tracking with a prescribed accuracy within a pregiven time at a preassigned convergence rate under any bounded initial condition, eliminating the long-standing initial condition dependence issue inherited with conventional prescribed performance control methods, and guaranteeing the predeterminability of convergence time simultaneously. Two simulation examples also demonstrate the effectiveness of the presented control strategy.

Robust Actor-Critic With Relative Entropy Regulating Actor

The accurate estimation of Q-function and the enhancement of agent's exploration ability have always been challenges of off-policy actor-critic algorithms. To address the two concerns, a novel robust actor-critic (RAC) is developed in this article. We first derive a robust policy improvement mechanism (RPIM) by using the local optimal policy about the current estimated Q-function to guide policy improvement. By constraining the relative entropy between the new policy and the previous one in policy improvement, the proposed RPIM can enhance the stability of the policy update process. The theoretical analysis shows that the incentive to increase the policy entropy is endowed when the policy is updated, which is conducive to enhancing the exploration ability of agents. Then, RAC is developed by applying the proposed RPIM to regulate the actor improvement process. The developed RAC is proven to be convergent. Finally, the proposed RAC is evaluated on some continuous-action control tasks in the MuJoCo platform and the experimental results show that RAC outperforms several state-of-the-art reinforcement learning algorithms.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle-pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle-pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle-pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle-pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

Exploration of s new biomarker in osteosarcoma and association with clinical outcomes: TOP2A+ cancer associated fibroblasts

BACKGROUND

Osteosarcoma (OS) is the leading malignant primary bone tumor in young adults and children and has a high mortality rate. Cancer-associated fibroblasts (CAFs) are major components of the tumor microenvironment, influencing cancer progression and metastasis. However, there is no systematic study on the role of CAF in OS.

METHODS

We collected six OS patients' single-cell RNA sequencing data from the TISCH database, which was processed using the Seurat package. We selected gene sets from the well-known MSigDB database and resorted to the clusterprofiler package for gene set enrichment analysis (GSEA). The least absolute shrinkage and selection operator (LASSO) regression model was used for identification of the variables. Receiver operating characteristic and decision curve analyses were utilized for determining the efficacy of the monogram model.

RESULTS

TOP2A+ CAFs was recognized as the carcinogenic CAFs subset, given its intense interaction with OS malignant cells and association with the critical cancer driver pathway. We intersected the differentially expressed genes of TOP2A+ CAFs with the prognostic genes selected from 88 OS samples. The acquired gene set was selected using the LASSO regression model and integrated with clinical factors to obtain a monogram model of high prognosis predicting power (area under the curve of 5 year survival at 0.883). Functional enrichment analysis revealed the detailed difference between two risk groups.

CONCLUSION

We identified TOP2A+ CAFs as a subset of oncogenic CAFs in OS. Based on differentially expressed genes derived from TOP2A+ CAFs, combined with bulk transcriptome prognostic genes, we constructed a risk model that can efficiently predict OS prognosis. Collectively, our study may provide new insights for future studies to elucidate the role of CAF in OS.

Efficacy and safety of mesenchymal stem cell therapy in liver cirrhosis: a systematic review and meta-analysis

AIM

Although the efficacy and safety of mesenchymal stem cell therapy for liver cirrhosis have been demonstrated in several studies. Clinical cases of mesenchymal stem cell therapy for patients with liver cirrhosis are limited and these studies lack the consistency of treatment effects. This article aimed to systematically investigate the efficacy and safety of mesenchymal stem cells in the treatment of liver cirrhosis.

METHOD

The data source included PubMed/Medline, Web of Science, EMBASE, and Cochrane Library, from inception to May 2023. Literature was screened by the PICOS principle, followed by literature quality evaluation to assess the risk of bias. Finally, the data from each study's outcome indicators were extracted for a combined analysis. Outcome indicators of the assessment included liver functions and adverse events. Statistical analysis was performed using Review Manager 5.4.

RESULTS

A total of 11 clinical trials met the selection criteria. The pooled analysis' findings demonstrated that both primary and secondary indicators had improved. Compared to the control group, infusion of mesenchymal stem cells significantly increased ALB levels in 2 weeks, 1 month, 3 months, and 6 months, and significantly decreased MELD score in 1 month, 2 months, and 6 months, according to a subgroup analysis using a random-effects model. Additionally, the hepatic arterial injection favored improvements in MELD score and ALB levels. Importantly, none of the included studies indicated any severe adverse effects.

CONCLUSION

The results showed that mesenchymal stem cell was effective and safe in the treatment of liver cirrhosis, improving liver function (such as a decrease in MELD score and an increase in ALB levels) in patients with liver cirrhosis and exerting protective effects on complications of liver cirrhosis and the incidence of hepatocellular carcinoma. Although the results of the subgroup analysis were informative for the selection of mesenchymal stem cells for clinical treatment, a large number of high-quality randomized controlled trials validations are still needed.

Uncertainty Estimation With Neural Processes for Meta-Continual Learning

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.

Anti-Martingale Proximal Policy Optimization

Since the sample data after one exploration process can only be used to update network parameters once in on-policy deep reinforcement learning (DRL), a high sample efficiency is necessary to accelerate the training process of on-policy DRL. In the proposed method, a submartingale criterion is proposed on the basis of the equivalence relationship between the optimal policy and martingale, and then an advanced value iteration (AVI) method is proposed to conduct value iteration with a high accuracy. Based on this foundation, an anti-martingale (AM) reinforcement learning framework is established to efficiently select the sample data that is conducive to policy optimization. In succession, an AM proximal policy optimization (AMPPO) method, which combines the AM framework with proximal policy optimization (PPO), is proposed to reasonably accelerate the updating process of state value that satisfies the submartingale criterion. Experimental results on the Mujoco platform show that AMPPO can achieve better performance than several state-of-the-art comparative DRL methods.

The value of hematocrit for predicting bronchopulmonary dysplasia in very low birth weight preterm infants

To determine hematocrit (HCT) and to identify independent risk factors for predicting bronchopulmonary dysplasia (BPD) in preterm infants with very low birth weight (VLBW) infants. This retrospective study included 296 premature infants with VLBW in the neonatal intensive care unit of the First Affiliated Hospital of the University of Science and Technology of China between January 2015 and December 2019. Maternal pregnant information and clinical information as well as hematological parameters of preterm babies were collected and compared. Then the maximum area under the curve of receiver operating characteristic curve was developed to estimate the predictive indicator in the blood. Finally, differential variables together with the predictive index were screened for multiple logistic regression analysis to determine independent prognostic factors for BPD. Infants were divided into a BPD group (134 cases) and a non-BPD group (162 cases). The area under the curve of HCT at postnatal 1 week was 0.737 with the sensitivity of 52.30 % and the specificity of 86.00%. Birth weight (BW) <1.12 kg, gestational age <28.4 weeks, newborn respiratory distress syndrome, mechanical ventilation ≥ 7 days, ventilation associated pneumonia, patent arterial duct, PaO2/FiO2 <300 mm Hg and HCT <0.455 at postnatal 1 week were risk factors for BPD of VLBW infants. HCT levels below 0.455 at 1 week after birth serve as a valuable indicator for the potential development of BPD.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.

Fabrication and application of molecularly imprinted polymer doped carbon dots coated silica stationary phase

Facing the difficulties in chromatographic separation of polar compounds, this investigation devotes to developing novel stationary phase. Molecularly imprinted polymers (MIPs) have aroused wide attention, owing to their outstanding selectivity, high stability, and low cost. In this work, a novel stationary phase based on carbon dots (CDs), MIP layer, and silica beads was synthesized to exploit high selectivity of MIPs, excellent physicochemical property of CDs, and outstanding chromatographic performances of silica microspheres simultaneously. The MIP doped CDs coated silica (MIP-CDs/SiO₂) stationary phase was systematically characterized by scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area measurement, and carbon elemental analysis. Furthermore, the chromatographic performance of the MIP-CDs/SiO₂ column was thoroughly assessed by using a wide variety of compounds (including nucleosides, sulfonamides, benzoic acids, and some other antibiotics). Meanwhile, the separation efficiency of the MIP-CDs/SiO₂ stationary phase was superior to other kinds of stationary phases (e.g. nonimprinted NIP-CDs/SiO₂, MIP/SiO₂, and C18-SiO₂). The results demonstrated that MIP-CDs/SiO₂ column exhibited best performance in terms of chromatographic separation. For all tested compounds, the resolution value was not less than 1.60, and the column efficiency of MIP-CDs/SiO₂ for thymidine was 22,740 plates/m. The results further indicate that the MIP-CDs/SiO₂ column can combine the good properties of MIP, CDs, with those of silica microbeads. Therefore, the developed MIP-CDs/SiO₂ stationary phase can be applied in the separation science and chromatography-based techniques.

Design, Synthesis, Antitumour Evaluation, and In Silico Studies of Pyrazolo-[1,5-c]quinazolinone Derivatives Targeting Potential Cyclin-Dependent Kinases

An efficient, straightforward, and metal-free methodology to rapidly access functionalised pyrazolo-[1,5-c]quinazolinones via a [3 + 2] dipolar cycloaddition and regioselective ring expansion process was developed. The synthesised compounds were characterised by methods such as NMR, HRMS, and HPLC. The in vitro antiproliferative activity against A549 cells (non-small cell lung cancer) was significant for compounds 4i, 4m, and 4n with IC50 values of 17.0, 14.2, and 18.1 μM, respectively. In particular, compounds 4t and 4n showed inhibitory activity against CDK9/2. Predicted biological target and molecular modelling studies suggest that the compound 4t may target CDKs for antitumour effects. The synthesised derivatives were considered to have moderate drug-likeness and sufficient safety in silico. In summary, a series of pyrazolo-[1,5-c]quinazolinone derivatives with antitumour activity is reported for the first time. We provide not only a simple and efficient synthetic method but also helpful lead compounds for the further development of novel cyclin-dependent kinase (CDK) inhibitors.

Boron decreases cadmium accumulation in water spinach by enhancing cadmium retention in the root cell walls

Cadmium (Cd) contamination and boron (B) deficiency are two major challenges associated with the farmland soils in Southern China. Therefore, this study was conducted to examine the impacts of B supply on Cd accumulation in water spinach (Ipomoea aquatica) using a cultivar (T308) with high Cd accumulation. The study further investigated the physiological mechanism behind the changes in Cd accumulation due to B supply. The findings revealed that B supply substantially reduced the Cd concentration in the leaves of water spinach by 41.20% and 37.16% under the Cd stress of 10 μM and 25 μM, respectively. Subcellular distribution of Cd showed that the Cd content as well as its proportion in root cell wall (RCW) increased significantly after B supply. Fourier transform infrared spectroscopy showed significant enrichment of negatively charged groups (such as -OH, -COOH, and -NH2) in the RCW after B supply. Overall, B supply also enhanced covalently bound pectin (CSP) content as well as the Cd content linked with CSP under Cd stress. These observations revealed that B regulated the Cd chelation in RCW, thereby reducing the amassment of Cd in water spinach.

Diversity and Abundance of Bacterial and Fungal Communities Inhabiting Camellia sinensis Leaf, Rhizospheric Soil, and Gut of Agriophara rhombata

Agriophara rhombata is a tea leaf moth that is considered one of the most destructive pests of Camellia sinensis (tea plant). Several recent studies have shown that many insects acquire part of the microbiome from their host and soil, but the pattern and diversity of their microbiome have not been clearly demonstrated. The present study aimed to investigate the bacterial and fungal communities present in the rhizospheric soil and leaf of tea plant compared to the gut of tea moth at different developmental stages (larvae, pupae, adult female and male) using Illumina MiSeq technology. Alpha diversity (Shannon index) showed higher (p < 0.05) bacterial and fungal diversity in soil samples than in leaf and tea moth larvae, pupae, and adult gut samples. However, during different developmental stages of tea moth, bacterial and fungal diversity did not differ (p > 0.05) between larvae, pupae, female, and male guts. Beta diversity also revealed more distinct bacterial and fungal communities in soil and leaf samples compared with tea moth gut samples, which had a more similar microbiome. Furthermore, Proteobacteria, Firmicutes, and Tenericutes were detected as the dominant bacterial phyla, while Ascomycota, Basidiomycota, and Mortierellomycota were the most abundant fungal phyla among all groups, but their relative abundance was comparatively higher (p < 0.05) in soil and leaf samples compared to tea moth gut samples. Similarly, Klebsiella, Streptophyta, and Enterococcus were the top three bacterial genera, while Candida, Aureobasidium, and Strelitziana were the top three fungal genera, and their relative abundance varied significantly (p < 0.05) among all groups. The KEGG analysis also revealed significantly higher (p < 0.5) enrichment of the functional pathways of bacterial communities in soil and leaf samples than in tea moth gut samples. Our study concluded that the bacterial and fungal communities of soil and tea leaves were more diverse and were significantly different from the tea moth gut microbiome at different developmental stages. Our findings contribute to our understanding of the gut microbiota of the tea moth and its potential application in the development of pest management techniques.