SpyGlass guided PDT for advanced Intraductal papillary mucinous neoplasm of the bile tract: A case report and literature review

Intraductal papillary mucinous neoplasm of the bile tract is a rare biliary tumor characterized by mucin growth within the bile duct. In the early stages, it often presents without significant obstruction, this often leads to its discovery in the advanced stages. We report a case of a 63-year-old female with an intraductal papillary mucinous neoplasm of the bile duct (IPMN-B). The patient had a history of intrahepatic bile duct stones and biliary ascariasis. She gradually developed symptoms such as jaundice and intermittent fever before admission, and a bile duct biopsy confirmed the diagnosis of IPMN-B. Currently, endoscopic photodynamic therapy (PDT) is considered an effective treatment for bile duct cancer. In this case, we performed two sessions of PDT guided by SpyGlass. The patient experienced complete remission postoperatively, and there has been no evidence of tumor recurrence or metastasis in the three years following the procedure.

ITPRIPL1 binds CD3ε to impede T cell activation and enable tumor immune evasion

Cancer immunotherapy has transformed treatment possibilities, but its effectiveness differs significantly among patients, indicating the presence of alternative pathways for immune evasion. Here, we show that ITPRIPL1 functions as an inhibitory ligand of CD3ε, and its expression inhibits T cells in the tumor microenvironment. The binding of ITPRIPL1 extracellular domain to CD3ε on T cells significantly decreased calcium influx and ZAP70 phosphorylation, impeding initial T cell activation. Treatment with a neutralizing antibody against ITPRIPL1 restrained tumor growth and promoted T cell infiltration in mouse models across various solid tumor types. The antibody targeting canine ITPRIPL1 exhibited notable therapeutic efficacy against naturally occurring tumors in pet clinics. These findings highlight the role of ITPRIPL1 (or CD3L1, CD3ε ligand 1) in impeding T cell activation during the critical "signal one" phase. This discovery positions ITPRIPL1 as a promising therapeutic target against multiple tumor types.

Therapeutic efficacy of acupuncture point stimulation for stomach cancer pain: a systematic review and meta-analysis

Purpose

In recent years, traditional Chinese medicine has received widespread attention in the field of cancer pain treatment. This meta-analysis is the first to evaluate the effectiveness and safety of acupuncture point stimulation in the treatment of stomach cancer pain.

Methods

For this systematic review and meta-analysis, we searched PubMed, Web of Science, Cochrane Library, Embase, WANFANG, China National Knowledge Infrastructure (CNKI), and Chinese Journal of Science and Technology (VIP) databases as well as forward and backward citations to studies published between database creation to July 27, 2023. All randomized controlled trials (RCTs) on acupuncture point stimulation for the treatment of patients with stomach cancer pain were included without language restrictions. We assessed all outcome indicators of the included trials. The evidence from the randomized controlled trials was synthesized as the standardized mean difference (SMD) of symptom change. The quality of the evidence was assessed using the Cochrane Risk of Bias tool. This study is registered on PROSPERO under the number CRD42023457341.

Results

Eleven RCTs were included. The study included 768 patients, split into 2 groups: acupuncture point stimulation treatment group (n = 406), medication control group (n = 372). The results showed that treatment was more effective in the acupuncture point stimulation treatment group than in the medication control group (efficacy rate, RR = 1.63, 95% CI 1.37 to 1.94, p < 0.00001), decreasing in NRS score was greater in acupuncture point stimulation treatment group than in the medication control group (SMD = -1.30, 95% CI -1.96 to -0.63, p < 0.001).

Systematic Review Registration

https://clinicaltrials.gov/, identifier CRD42023457341.

Instantaneous frequency measurement based on photonic compressive sensing with sub-Nyquist pseudo-random binary sequences

We propose a novel, to the best of our knowledge, approach to realizing instantaneous frequency measurement with ultrahigh measurement bandwidth, which utilizes three-channel photonic compressive sensing (CS) with sub-Nyquist pseudo-random binary sequences (PRBSs). In each CS channel, an alias frequency is recovered due to the sub-Nyquist property of the applied PRBS. A frequency identification algorithm is employed to determine the frequency of the signal under measurement according to the three alias frequencies. The proposed approach significantly reduces the bit rate of the applied PRBSs and the sampling rate required by the digitizers in CS. A proof-of-concept experiment for measuring frequency in the Ku band is demonstrated using PRBSs at 1 Gb/s and digitizers with a sampling rate of 250 MS/s.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors.

AIM

To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients.

METHODS

This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA).

RESULTS

The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit.

CONCLUSION

Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Illuminating the immunological landscape: mitochondrial gene defects in pancreatic cancer through a multiomics lens

This comprehensive review delves into the complex interplay between mitochondrial gene defects and pancreatic cancer pathogenesis through a multiomics approach. By amalgamating data from genomic, transcriptomic, proteomic, and metabolomic studies, we dissected the mechanisms by which mitochondrial genetic variations dictate cancer progression. Emphasis has been placed on the roles of these genes in altering cellular metabolic processes, signal transduction pathways, and immune system interactions. We further explored how these findings could refine therapeutic interventions, with a particular focus on precision medicine applications. This analysis not only fills pivotal knowledge gaps about mitochondrial anomalies in pancreatic cancer but also paves the way for future investigations into personalized therapy options. This finding underscores the crucial nexus between mitochondrial genetics and oncological immunology, opening new avenues for targeted cancer treatment strategies.

Harmonic active mode-locking optoelectronic oscillator with suppressed supermode noise based on pulse intensity feedback

A harmonic active mode-locking optoelectronic oscillator (HAML-OEO) with pulse intensity feedback is proposed and experimentally demonstrated. It is capable of generating microwave pulses characterized by suppressed supermode noise, uniform intensity, and tunable repetition rates. Unlike traditional HAML-OEOs, active mode-locking and pulse intensity feedback are simultaneously achieved through the use of a dual-drive Mach-Zehnder modulator (DDMZM). By synchronously feeding back the generated microwave pulses to the DDMZM, each pulse undergoes a loss proportional to its intensity, facilitating pulse intensity equalization and supermode noise suppression. In the experiment, intensity-equalized microwave pulse trains with repetition rates of 499 kHz and 998 kHz are generated by the 5th- and 10th-order HAML-OEOs, respectively, with the measured supermode noise suppression ratios exceeding 40 dB.

Acupuncture for insomnia symptoms in hypertensive patients: a systematic review and meta-analysis

Purpose

In the realm of pain management, traditional Chinese medicine, specifically acupuncture, has garnered increasing attention. This meta-analysis pioneers the evaluation of acupuncture's effectiveness in treating insomnia among hypertensive patients.

Methods

We conducted a comprehensive search across several databases-PubMed, Web of Science, Cochrane Library, WANFANG, China National Knowledge Infrastructure (CNKI), Sinomed, and the Chinese Journal of Science and Technology (VIP). Additionally, forward and backward articles of studies published from the inception of these databases until 10 September 2023, were reviewed. This systematic review and meta-analysis included all randomized controlled trials (RCTs) focusing on acupuncture for insomnia in hypertensive patients, without imposing language or date restrictions. We rigorously assessed all outcome measures reported in these trials. The evidence was synthesized by calculating the difference between mean differences (MD) in symptom change. The quality of the evidence was determined using the Cochrane Risk of Bias tool. This study is registered with PROSPERO under number CRD42023461760.

Results

Our analysis included 16 RCTs, comprising 1,309 patients. The findings revealed that acupuncture was significantly more effective than the control group in reducing insomnia symptoms, as indicated by a greater decrease in the PSQI score (MD = -3.1, 95% CI [-3.77 to -2.62], p < 0.00001). Additionally, improvements in both systolic and diastolic blood pressure were more pronounced in the acupuncture group compared to the control group (SBP: MD = -10.31, 95% CI [-16.98 to -3.64], p = 0.002; DBP: MD = -5.71, 95% CI [-8.19 to -3.23], p < 0.00001). These results suggest that acupuncture not only improves sleep quality but also lowers blood pressure in patients suffering from hypertension and insomnia. Further research is warranted to elucidate optimal acupuncture points and the duration of treatment for maximized therapeutic effect.Systematic review registration:https://www.crd.york.ac.uk/prospero, CRD42023461760.

Unveiling the immune symphony: decoding colorectal cancer metastasis through immune interactions

Colorectal cancer (CRC), known for its high metastatic potential, remains a leading cause of cancer-related death. This review emphasizes the critical role of immune responses in CRC metastasis, focusing on the interaction between immune cells and tumor microenvironment. We explore how immune cells, through cytokines, chemokines, and growth factors, contribute to the CRC metastasis cascade, underlining the tumor microenvironment's role in shaping immune responses. The review addresses CRC's immune evasion tactics, especially the upregulation of checkpoint inhibitors like PD-1 and CTLA-4, highlighting their potential as therapeutic targets. We also examine advanced immunotherapies, including checkpoint inhibitors and immune cell transplantation, to modify immune responses and enhance treatment outcomes in CRC metastasis. Overall, our analysis offers insights into the interplay between immune molecules and the tumor environment, crucial for developing new treatments to control CRC metastasis and improve patient prognosis, with a specific focus on overcoming immune evasion, a key aspect of this special issue.

Measuring discrepancies between simple medullary and synchronous medullary/papillary thyroid carcinomas: a comparative cross-sectional study

Objective

To study the clinicopathological characteristics of patients with synchronous medullary and papillary thyroid carcinomas.

Methods

The clinical data of patients with medullary thyroid carcinoma (MTC) operated in our hospital (Department of Thyroid Surgery, China-Japan Union Hospital, Jilin University) from February 2009 to February 2023 were evaluated using an analytical review approach. They were divided into an observation group (patients with synchronous MTC and papillary thyroid carcinoma PTC) and a control group (simple MTC) according to whether the clinical data were associated with MTC, in order to compare the clinical features, pathological types, stage characteristics and molecular biology characteristics of the two groups and to investigate the follow-up of the two groups.

Results

The study included 122 MTC, 30 with synchronous MTC/PTC and 92 simple MTC. When the data were compared, the sex ratio, preoperative calcitonin level, preoperative CEA level, presence of calcifications in the MTC lesions, surgical methods, number of MTC lesions, presence of nodular goitre and presence of thyroiditis were higher in the observation group than in the control group. There was a significant difference between the groups when the MTC lesion diameter was ≤1cm in terms of preoperative CEA value (P<0.05); when the MTC lesion diameter was >1cm, there was a statistical difference between the two groups in terms of preoperative Ctn value (P<0.05). Type III was significantly different from the simple group, while type IV was more similar to the simple group. The preoperative serum Ctn value was positively correlated with maximum tumour diameter in both groups, although the correlation was stronger in the easy group. Preoperative CEA was positively correlated with maximum tumour diameter in both groups, with a stronger correlation in the combination group. Preoperative Ctn and CEA were positively correlated with lymph node metastasis in the simple group, whereas there was no apparent correlation with lymph node metastasis in the combination group. The cut-off value of preoperative serum Ctn for cervical lymph node metastases in the simple group was 39.2pg/ml and for lateral cervical lymph node metastases 195.5pg/ml. The cut-off value of preoperative serum Ctn for cervical lymph node metastases in the combination group was 60.79pg/ml and for lateral cervical lymph node metastases 152.6pg/ml. In the simple group, prognosis was significantly worse in the progression group (P<0.001), with no statistical difference between the remission and stable groups. In the combination group, the prognosis of the progression and stable groups was significantly worse than that of the remission group (P<0.001), with no statistical difference between the progression and stable groups.

Conclusion

In patients with synchronous medullary and papillary thyroid carcinomas, preoperative Ctn and CEA levels, calcifications, solitary lesions, combined goitre or thyroiditis differ significantly from simple MTC. Therefore, clinical management should pay attention to the above factors and early risk screening should be performed to improve prognosis as much as possible.

Discovery of galectin-8 as an LILRB4 ligand driving M-MDSCs defines a class of antibodies to fight solid tumors

LILRB4 is an immunosuppressive receptor, and its targeting drugs are undergoing multiple preclinical and clinical trials. Currently, the absence of a functional LILRB4 ligand in solid tumors not only limits the strategy of early antibody screening but also leads to the lack of companion diagnostic (CDx) criteria, which is critical to the objective response rate in early-stage clinical trials. Here, we show that galectin-8 (Gal-8) is a high-affinity functional ligand of LILRB4, and its ligation induces M-MDSC by activating STAT3 and inhibiting NF-κB. Significantly, Gal-8, but not APOE, can induce MDSC, and both ligands bind LILRB4 noncompetitively. Gal-8 expression promotes in vivo tumor growth in mice, and the knockout of LILRB4 attenuates tumor growth in this context. Antibodies capable of functionally blocking Gal-8 are able to suppress tumor growth in vivo. These results identify Gal-8 as an MDSC-driving ligand of LILRB4, and they redefine a class of antibodies for solid tumors.

XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis

X-ray repair cross-complementation group 1 (XRCC1) is a pivotal contributor to base excision repair, and its dysregulation has been implicated in the oncogenicity of various human malignancies. However, a comprehensive pan-cancer analysis investigating the prognostic value, immunological functions, and epigenetic associations of XRCC1 remains lacking. To address this knowledge gap, we conducted a systematic investigation employing bioinformatics techniques across 33 cancer types. Our analysis encompassed XRCC1 expression levels, prognostic and diagnostic implications, epigenetic profiles, immune and molecular subtypes, Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), immune checkpoints, and immune infiltration, leveraging data from TCGA, GTEx, CELL, Human Protein Atlas, Ualcan, and cBioPortal databases. Notably, XRCC1 displayed both positive and negative correlations with prognosis across different tumors. Epigenetic analysis revealed associations between XRCC1 expression and DNA methylation patterns in 10 cancer types, as well as enhanced phosphorylation. Furthermore, XRCC1 expression demonstrated associations with TMB and MSI in the majority of tumors. Interestingly, XRCC1 gene expression exhibited a negative correlation with immune cell infiltration levels, except for a positive correlation with M1 and M2 macrophages and monocytes in most cancers. Additionally, we observed significant correlations between XRCC1 and immune checkpoint gene expression levels. Lastly, our findings implicated XRCC1 in DNA replication and repair processes, shedding light on the precise mechanisms underlying its oncogenic effects. Overall, our study highlights the potential of XRCC1 as a prognostic and immunological pan-cancer biomarker, thereby offering a novel target for tumor immunotherapy.

Immunoregulatory functions and therapeutic potential of natural killer cell-derived extracellular vesicles in chronic diseases

Extracellular vesicles (EVs) have been proven to play a significant immunoregulatory role in many chronic diseases, such as cancer and immune disorders. Among them, EVs derived from NK cells are an essential component of the immune cell functions. These EVs have been demonstrated to carry a variety of toxic proteins and nucleic acids derived from NK cells and play a therapeutic role in diseases like malignancies, liver fibrosis, and lung injury. However, natural NK-derived EVs (NKEVs) have certain limitations in disease treatment, such as low yield and poor targeting. Concurrently, NK cells exhibit characteristics of memory-like NK cells, which have stronger proliferative capacity, increased IFN-γ production, and enhanced cytotoxicity, making them more advantageous for disease treatment. Recent research has shifted its focus towards engineered extracellular vesicles and their potential to improve the efficiency, specificity, and safety of disease treatments. In this review, we will discuss the characteristics of NK-derived EVs and the latest advancements in disease therapy. Specifically, we will compare different cellular sources of NKEVs and explore the current status and prospects of memory-like NK cell-derived EVs and engineered NKEVs.

Photonic generation and transmission for an X-band dual-chirp waveform with frequency multiplication and power-fading compensation

The generation of an X-band dual-chirp waveform, which is capable of pulse compression, plays an important role in radar, electric warfare, and satellite communication systems. With the development of applications such as multi-static radar, transmission over long distances has attracted considerable attention. In this paper, a photonic system for X-band dual-chirp waveform generation and transmission based on frequency multiplication and power-fading compensation is put forward and experimentally carried out. Based on a compact dual-parallel Mach-Zehnder modulator (DPMZM), the dual-chirp waveforms of 8.6-9.6 GHz and 9.6-10.6 GHz are generated by an RF carrier of 4.8 GHz and transmitted through a 40 km single-mode fiber (SMF) spool. The dispersion-induced power fading of the chirp waveform is compensated for by about 13 dB. The full width at half maximum (FWHM) and the peak-to-sidelobe ratio (PSR) of the compressed pulses are 1 ns and 11.5 dB, respectively. Moreover, the compensation of power fading in the entire X-band is verified to demonstrate the applicability of our system. By flexibly adjusting the bias voltage of the built-in phase shifter, the system can be applied in more scenarios.

Formulation, Preparation, and Evaluation of Bifunctional Micelle with Glycyrrhizic Acid Containing Emodin for Toxicity Attenuation Application

OBJECTIVE

To prepare GA-Emo micelles and investigate the feasibility of using GA as both a bifunctional drug and carrier.

METHODS

The preparation of GA-Emo micelles was accomplished via the thin-film dispersion method with GA as the carrier. Size distribution, entrapment efficiency, and drug loading were used to evaluate the characteristics of micelles. The absorption and transport properties of the micelles in Caco-2 cells were investigated, while their pharmacodynamics in mice were preliminarily studied.

RESULTS

The optimal formulation featured a GA/Emo in weight ratio of 2:1 and an encapsulation efficiency of 23.68%. The optimized GA/Emo was characterized as small uniform spheres with an average micellar size of 168.64 ± 5.69 nm, a polydispersity index of 0.17 ± 0.01, and an electrically negative surface (-35.33 ± 0.94 mV). Absorption and transport experiments with Caco-2 cells showed that the absorption of GA-Emo micelles in small intestines was mainly passive transport, amid their transport volume being significantly higher than that of Emo monomer. The intestinal wall thickness of the GAEmo micelles group was significantly lower than that of the Emo group, which meant that the colonic toxicity of the micelles was lower than unincorporated Emo.

CONCLUSION

The advantages of GA as a bifunctional micelle carrier in formulation characters, drug release, and toxicity attenuation provide a new idea for the application of the GA of natural medicine in drug delivery for toxicity reduction.

Photonic distributed compressive sampling of multi-node wideband sparse radio frequency signals

A photonic distributed compressive sampling (PDCS) approach for identifying the spectra of multi-node wideband sparse signals is proposed. The scheme utilizes wavelength division multiplexing (WDM) technology to transmit multi-node signals to a central station, where distributed compressive sampling (DCS) based on the random demodulator (RD) model is employed to simultaneously identify the signal spectrum. By exploiting signal correlations among nodes, DCS achieves a higher compression ratio of the sampling rate than single-node compressive sampling (CS). In a semi-physical simulation experiment, we demonstrate the feasibility of the approach by recovering the spectra of two wideband sparse signals from nodes located 20 km and 10 km away. The spectra of two signals with a mixed support-set sparsity of 2 and 4 are recovered with a compression ratio of 8 and 4, respectively. We further investigate the impact of common parts and the number of nodes on PDCS performance through numerical simulation. The proposed system takes advantage of the ultra-high bandwidth of photonic technology and the low loss of optical fiber transmission, making it suitable for long-distance, multi-node, and large-coverage electromagnetic spectrum identification.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma

Background

Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC.

Methods

Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments.

Results

By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs.

Conclusions

In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

MnF2 Surface Modulated Hollow Carbon Nanorods on Porous Carbon Nanofibers as Efficient Bi-Functional Oxygen Catalysis for Rechargeable Zinc-Air Batteries

Developing highly efficient bi-functional noble-metal-free oxygen electrocatalysts with low-cost and scalable synthesis approach is challenging for zinc-air batteries (ZABs). Due to the flexible valence state of manganese, MnF2 is expected to provide efficient OER. However, its insulating properties may inhibit its OER process to a certain degree. Herein, during the process of converting the manganese source in the precursor of porous carbon nanofibers (PCNFs) to manganese fluoride, the manganese source is changed to manganese acetate, which allows PCNFs to grow a large number of hollow carbon nanorods (HCNRs). Meanwhile, manganese fluoride will transform from the aggregation state into uniformly dispersed MnF2 nanodots, thereby achieving highly efficient OER catalytic activity. Furthermore, the intrinsic ORR catalytic activity of the HCNRs/MnF2 @PCNFs can be enhanced due to the charge modulation effect of MnF2 nanodots inside HCNR. In addition, the HCNRs stretched toward the liquid electrolyte can increase the capture capacity of dissolved oxygen and protect the inner MnF2 , thereby enhancing the stability of HCNRs/MnF2 @PCNFs for the oxygen electrocatalytic process. MnF2 surface-modulated HCNRs can strongly enhance ORR activity, and the uniformly dispersed MnF2 can also provide higher OER activity. Thus, the prepared HCNRs/MnF2 @PCNFs obtain efficient bifunctional oxygen catalytic ability and high-performance rechargeable ZABs.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Photonic generation of dual-mode multi-format chirp microwave signals

In this paper, we present a novel, to the best of our knowledge, photonic scheme for the generation of dual-mode multi-format chirp microwave signals, utilizing a dual-drive dual-parallel Mach-Zehnder modulator (DD-DPMZM). By inputting a single-chirp signal and controlling the input binary sequences, the proposed method can generate up-, down-, dual-, or triangular-chirp signals in both pulse and continuous-wave modes. Moreover, the duty cycle of the generated chirp signals in the pulse mode can be easily adjusted by manipulating the injected binary sequences. The compact structure of the proposed scheme eliminates the need for polarization control in signal switching and avoids the use of any optical filter. Experimental verification confirms the feasibility of our approach, while also pointing towards its promising applications in multi-functional radar systems.

Trust-aware conditional adversarial domain adaptation with feature norm alignment

Adversarial learning has proven to be an effective method for capturing transferable features for unsupervised domain adaptation. However, some existing conditional adversarial domain adaptation methods assign equal importance to different samples, ignoring the fact that hard-to-transfer samples might damage the conditional adversarial adaptation procedure. Meanwhile, some methods can only roughly align marginal distributions across domains, but cannot ensure category distributions alignment, causing classifiers to make uncertain or even wrong predictions for some target data. Furthermore, we find that the feature norms of real images usually follow a complex distribution, so directly matching the mean feature norms of two domains cannot effectively reduce the statistical discrepancy of feature norms and may potentially induce feature degradation. In this paper, we develop a Trust-aware Conditional Adversarial Domain Adaptation (TCADA) method for solving the aforementioned issues. To quantify data transferability, we suggest utilizing posterior probability modeled by a Gaussian-uniform mixture, which effectively facilitates conditional domain alignment. Based on this posterior probability, a confidence-guided alignment strategy is presented to promote precise alignment of category distributions and accelerate the learning of shared features. Moreover, a novel optimal transport-based strategy is introduced to align the feature norms and facilitate shared features becoming more informative. To encourage classifiers to make more accurate predictions for target data, we also design a mixed information-guided entropy regularization term to promote deep features being away from the decision boundaries. Extensive experiments show that our method greatly improves transfer performance on various tasks.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

Deciphering the role of HPV-mediated metabolic regulation in shaping the tumor microenvironment and its implications for immunotherapy in HNSCC

Head and neck squamous cell carcinoma (HNSCC), as a complex and variable malignancy, poses a significant threat to human health. Since the intricate association between HPV and HNSCC emerged, its role within the TME has garnered extensive attention. HPV+HNSCC exhibits distinct immunological characteristics within the TME, intricately intertwined with mechanisms of immune evasion. HPV employs multifaceted pathways to intervene in metabolic regulation within the TME, exerting influence over immune cell functionality and neoplastic cell genesis. Furthermore, the heightened immune reactivity exhibited by HPV+HNSCC within the TME augments responses to immune interventions such as immune checkpoint inhibitors. Therefore, amidst the current limitations of therapeutic approaches, immunotherapy stands as a promising strategy to overcome the conventional confines of treating HNSCC. This article comprehensively outlines the impact of HPV on the inception and progression of HNSCC while discussing the amalgamation of metabolic regulation within the TME and immunotherapeutic strategies. By intervening in the reciprocal interactions between HPV and HNSCC within the TME, the potential to modulate the efficacy of immune-based treatments becomes evident. Concurrently, a synthesis of pertinent biomarker development is summarized. Such endeavors hold paramount significance for personalized therapeutic approaches and the more effective management of HNSCC patients.

A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer

BACKGROUND

The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC).

METHODS

Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature.

RESULTS

Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy.

CONCLUSION

The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy

BACKGROUND

Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer.

METHODS

Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape.

RESULTS

The constructed signature reveals a pronounced disparity in prognosis between the high- and low-risk groups of LUAD patients. The statistical discrepancies in survival times among LUAD patients from both the TCGA and GEO databases further corroborate this observation. Additionally, heightened levels of immune cell infiltration expression are evidenced in the low-risk group, suggesting a potential benefit from immunotherapeutic interventions for these patients. The expression levels of pivotal risk-associated genes in tissue samples were assessed utilizing qRT-PCR, thereby unveiling PITX3 as a plausible therapeutic target in the context of LUAD.

CONCLUSIONS

Our constructed ICD-related signatures provide help in predicting the prognosis and immunotherapy of LUAD patients, and to some extent guide the clinical treatment of LUAD patients.

Unraveling molecular networks in thymic epithelial tumors: deciphering the unique signatures

Thymic epithelial tumors (TETs) are a rare and diverse group of neoplasms characterized by distinct molecular signatures. This review delves into the complex molecular networks of TETs, highlighting key aspects such as chromosomal abnormalities, molecular subtypes, aberrant gene mutations and expressions, structural gene rearrangements, and epigenetic changes. Additionally, the influence of the dynamic tumor microenvironment on TET behavior and therapeutic responses is examined. A thorough understanding of these facets elucidates TET pathogenesis, offering avenues for enhancing diagnostic accuracy, refining prognostic assessments, and tailoring targeted therapeutic strategies. Our review underscores the importance of deciphering TETs' unique molecular signatures to advance personalized treatment paradigms and improve patient outcomes. We also discuss future research directions and anticipated challenges in this intriguing field.

Photonic angle-of-arrival measurement of microwave signals using a triangular wave

A novel, to the best of our knowledge, photonics-assisted approach for measuring the angle-of-arrival (AOA) of microwave signals with a large measurement range is proposed. The system utilizes a dual-drive Mach-Zehnder modulator (DDMZM) for signal modulation, followed by an optical filter for sideband selection. The dc port of the DDMZM is fed by a triangular wave to generate a pair of opposite frequency shifts. An intermediate frequency (IF) signal with a periodic phase jump is obtained after photodetection. The AOA of the incoming signals can be estimated by measuring the value of the phase jump. The key novelty of the proposed scheme lies in the application of a triangular wave to shift the frequency of optical signals, which introduces a periodic phase jump to the IF signal. The system incorporates only a single optical channel, largely reducing the system complexity. Experimental results show that an AOA measurement with a range of -70.8° to 70.8° is realized, with errors confined to within ±2°.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

Effect of external beam radiation therapy versus transcatheter arterial chemoembolization for non-diffuse hepatocellular carcinoma (≥ 5 cm): a multicenter experience over a ten-year period

Background

The optimal local treatment for HCC with tumor diameter ≥ 5 cm is not well established. This research evaluated the effectiveness of external beam radiation therapy (EBRT) versus transcatheter arterial chemoembolization (TACE) for HCC with tumor diameter ≥ 5 cm.

Methods

A total of 1210 HCC patients were enrolled in this study, including 302 and 908 patients that received EBRT and TACE, respectively. Propensity score matching (PSM) was used to identify patient pairs with similar baseline characteristics. Overall survival (OS) was the primary study endpoint.

Results

We identified 428 patients using 1:1 PSM for survival comparison. Compared with the TACE group, the EBRT group had a significantly longer median OS (mOS) before (14.9 vs. 12.3 months, p = 0.0085) and after (16.8 vs. 11.4 months, p = 0.0026) matching. In the subgroup analysis, compared with the TACE group, the EBRT group had a significantly longer mOS for HCC with tumor diameters of 5-7 cm (34.1 vs. 14.3 months, p = 0.04) and 7-10 cm (34.4 vs. 10 months, p = 0.00065), whereas for HCC with tumor diameters ≥ 10 cm, no significant difference in mOS was observed (11.2 vs. 11.2 months, p = 0.83). In addition, the multivariable Cox analysis showed that Child-A, alkaline phosphatase < 125 U/L, and EBRT were independent prognostic indicators for longer survival.

Conclusion

EBRT is more effective than TACE as the primary local treatment for HCC with tumor diameter ≥ 5 cm, especially for HCC with tumor diameter of 5-10 cm.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

M2 macrophage inhibits the antitumor effects of Lenvatinib on intrahepatic cholangiocarcinoma

Background and objectives

The relationship between the tumor microenvironment and the network of key signaling pathways in cancer plays a key role in the occurrence and development of tumors. Tumor-associated macrophages (TAMs) are important inflammatory cells in the tumor microenvironment and play an important role in tumorigenesis and progression. Macrophages in malignant tumors, mainly the M2 subtype, promote tumor progression by producing cytokines and down-regulating anti-inflammatory immune responses. Several articles have investigated the effect of macrophages on the sensitivity of cancer chemotherapeutic agents, but few such articles have been reported in cholangiocarcinoma, so we investigated the effect of M2 macrophage on the sensitivity of cholangiocarcinoma cells to Lenvatinib compared to M1.

Methods

THP-1 monocytes were polarized to M0 macrophage by phorbol 12-myristate 13-acetate (PMA) and then induced to differentiate into M1 and M2 macrophages by LPS, IFN-γ and IL-4 and IL-13, respectively. Macrophages and cholangiocarcinoma cells were co-cultured prior to 24 hours of Lenvatinib administration, cancer cell apoptosis was detected by western-blot, FACS analysis of Annexin V and PI staining. Furthermore, we use xCELLigence RTCA SP Instrument (ACEA Bio-sciences) to monitor cell viability of Lenvatinib administration in co-culture of cholangiocarcinoma cells and macrophages. After tumorigenesis in immunodeficient mice, Lenvatinib was administered, and the effects of M2 on biological characteristics of cholangiocarcinoma cells were investigated by immuno-histochemistry.

Results

mRNA and protein expression of M1 and M2 markers confirmed the polarization of THP-1 derived macrophages, which provided a successful and efficient model of monocyte polarization to TAMs. Lenvatinib-induced apoptosis of cholangiocarcinoma cells was significantly reduced when co-cultured with M2 macrophage, whereas apoptosis of cholangiocarcinoma cells co-cultured with M1 macrophage was increased. In the CDX model, Lenvatinib-induced cancer cell apoptosis was markedly reduced, and proliferative cells increased in the presence of M2 macrophages. Angiogenesis related factors was significantly increased in cholangiocarcinoma cells co-cultured with M2.

Conclusion

Compared with M1, M2 macrophages can inhibit the anti-tumor effect of Lenvatinib on cholangiocarcinoma through immune regulation, which may be related to the tumor angiogenesis factor effect of M2 macrophage.

External radiotherapy combined with sorafenib has better efficacy in unresectable hepatocellular carcinoma: a systematic review and meta-analysis

Advanced hepatocellular carcinoma (HCC) has a very low resectable rate. This meta-analysis aimed to compare efficacy of three combination strategies in treatment of advanced unresectable HCC with a view of guiding future selection of the best combination therapy for sorafenib and local therapy. A search was conducted to identify relevant literature published between April 2013 and May 2022, and then compared efficacy of sorafenib combined with external radiotherapy (SOF + RT), sorafenib with transarterial chemoembolization (SOF + TACE), sorafenib with hepatic artery infusion chemotherapy (SOF + HAIC), sorafenib (SOF), external radiotherapy (RT), transarterial chemoembolization (TACE), and hepatic artery infusion chemotherapy (HAIC) were studied and analyzed. Finally, the results were statistically analyzed using R 3.5.3 software and Stata/SE 15.0 software. A total of 46 studies, involving 7595 patients, were included in the meta-analysis. Analysis of overall survival (OS) and progression-free survival (PFS) of seven related treatment interventions revealed that the combination therapy had significantly higher efficacy than monotherapies. Among the combination therapies, SOF + RT was associated with the best OS and PFS rates, and the least adverse events compared to the other treatment modalities. The efficacy of combination therapy was better than monotherapy. In combination therapy, the overall survival time and progression-free survival time of SOF + RT were longer, and the adverse reactions were less. Therefore, SOF + RT may be the best choice for sorafenib combined with local therapy.

Recent development of polymer nanomicelles in the treatment of eye diseases

The eye, being one of the most intricate organs in the human body, hosts numerous anatomical barriers and clearance mechanisms. This highlights the importance of devising a secure and efficacious ocular medication delivery system. Over the past several decades, advancements have been made in the development of a nano-delivery platform based on polymeric micelles. These advancements encompass diverse innovations such as poloxamer, chitosan, hydrogel-encapsulated micelles, and contact lenses embedded with micelles. Such technological evolutions allow for sustained medication retention and facilitate enhanced permeation within the eye, thereby standing as the avant-garde in ocular medication technology. This review provides a comprehensive consolidation of ocular medications predicated on polymer nanomicelles from 2014 to 2023. Additionally, it explores the challenges they pose in clinical applications, a discussion intended to aid the design of future clinical research concerning ocular medication delivery formulations.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

Background

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

Methods

In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

Results

Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

Conclusion

Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

CD8 + T-cell marker genes reveal different immune subtypes of oral lichen planus by integrating single-cell RNA-seq and bulk RNA-sequencing

BACKGROUND

Oral lichen planus (OLP) is a local autoimmune disease induced by T-cell dysfunction that frequently affects middle-aged or elderly people, with a higher prevalence in women. CD8 + T cells, also known as killer T cells, play an important role in the progression and persistence of OLP. In order to identify different OLP subtypes associated with CD8 + T cell pathogenesis, consensus clustering was used.

METHODS

In this study, we preprocessed and downscaled the OLP single-cell dataset GSE211630 cohort downloaded from Gene Expression Omnibus (GEO) to finally obtain the marker genes of CD8 + T cells. Based on the expression of marker genes, we classified OLP patients into CMGs subtypes using unsupervised clustering analysis. The gene expression profiles were analyzed by WGCNA using the "WGCNA" R package based on the clinical disease traits and typing results, and 108 CD8 + T-cell related OLP pathogenicity-related genes were obtained from the intersection. Patients were once again classified into gene subtypes based on intersection gene expression using unsupervised clustering analysis.

RESULTS

After obtaining the intersecting genes of CD8 + T cells related to pathogenesis, OLP patients can be precisely classified into two different subtypes based on unsupervised clustering analysis, and subtype B has better immune infiltration results, providing clinicians with a reference for personalized treatment.

CONCLUSIONS

Classification of OLP into different subtypes improve our current understanding of the underlying pathogenesis of OLP and provides new insights for future studies.

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis

Background

The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis.

Methods

We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations.

Results

We discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status.

Conclusion

This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis.

Advances in HPV-associated tumor management: Therapeutic strategies and emerging insights

With the rapid increase in the incidence of cervical cancer, anal cancer and other cancers, human papillomavirus (HPV) infection has become a growing concern. Persistent infection with high-risk HPV is a major cause of malignant tumors. In addition, microbiota and viruses such as human immunodeficiency virus, herpes simplex virus, and Epstein-Barr virus are closely associated with HPV infection. The limited effectiveness of existing treatments for HPV-associated tumors and the high rates of recurrence and metastasis in patients create an urgent need for novel and effective approaches. In recent years, HPV vaccine coverage has increased and can reduce the incidence of serious adverse events. Overall, this article provides a comprehensive overview of HPV biology, microbiome, and other viral interactions in cancer development, highlighting the need for a more comprehensive approach to cancer prevention and treatment. Current and emerging HPV-related cancer control and treatment strategies are also further explored.

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

Background

Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer.

Methods

The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the "irGSEA" R package. The estimation of cell cycle was conducted with the "tricycle" R package, and intercellular communication networks were analyzed using "CellChat". For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized.

Results

Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression.

Conclusion

The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study

Background

TACE and TACE with or without targeted immunotherapy are crucial comprehensive therapies for middle and advanced HCC. However, a reasonable and concise score is needed to evaluate TACE and TACE combined with systemic therapy in HCC treatment.

Methods

The HCC patients were grouped into two groups: training group (n = 778) (treated with TACE) and verification group (n = 333). The predictive value of baseline variables on overall survival was analyzed using COX model, and easy-to-use ALR (AST and Lym-R) scores. The best cut-off value of AST and Lym-R were determined using X-Tile software based on total survival time (OS) and further verified via a restricted three-spline method. Meanwhile, the score was further verified using two independent valid sets: TACE combined with targeted therapy and TACE with targeted combined immunotherapy.

Results

In multivariate analysis, baseline serum AST>57.1 (p < 0.001) and Lym-R≤21.7 (p < 0.001) were identified as independent prognostic factors. The OS of patients in the TACE pooled cohort with 0, 1, and 2 scores were 28.1 (95% CI 24-33.8) months, 15 (95% CI 12.4-18.6) months, and 7.4 (95% CI 5.7-9.1) months, respectively. The time-varying ROC curve based on ALR showed that the AUC values for predicting 1, -2-and 3-year OS were 0.698, 0.718, and 0.636, respectively. These results are confirmed in two independent valid sets of TACE combined with targeted therapy and TACE with targeted combined immunotherapy. And we established a nomogram after COX regression to predict the 1 -, 2- and 3-year survival time.

Conclusion

Our study confirmed that ALR score can predict the prognosis of HCC treated with TACE or TACE combined with systemic therapy.

Retraction of "0D/2D Coordination Complexes: Magnetic Studies, Protection, and Mechanism against Ischemic Myocardial Damage by Reducing the Activation of the PI3K/Akt Signaling Pathway"

[This retracts the article DOI: 10.1021/acsomega.0c05601.].

Revolutionizing anti-tumor therapy: unleashing the potential of B cell-derived exosomes

B cells occupy a vital role in the functioning of the immune system, working in tandem with T cells to either suppress or promote tumor growth within the tumor microenvironment(TME). In addition to direct cell-to-cell communication, B cells and other cells release exosomes, small membrane vesicles ranging in size from 30-150 nm, that facilitate intercellular signaling. Exosome research is an important development in cancer research, as they have been shown to carry various molecules such as major histocompatibility complex(MHC) molecules and integrins, which regulate the TME. Given the close association between TME and cancer development, targeting substances within the TME has emerged as a promising strategy for cancer therapy. This review aims to present a comprehensive overview of the contributions made by B cells and exosomes to the tumor microenvironment (TME). Additionally, we delve into the potential role of B cell-derived exosomes in the progression of cancer.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Background

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

Results

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

Conclusions

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

Photonic compressive sampling of wideband sparse radio frequency signals with 1-bit quantization

Photonic compressive sampling (PCS) is an effective method to recover wideband sparse radio frequency (RF) signals. However, the noisy and high-loss photonic link leads to signal-to-noise ratio (SNR) degradation of the RF signal to be tested, which limits the recovery performance of the PCS system. In this paper, a random demodulator-based PCS system with 1-bit quantization is proposed. The system consists of a photonic mixer, a low-pass filter, a 1-bit analog-to-digital converter (ADC), and a digital signal processor (DSP). The 1-bit quantized result is used to recover the spectra of the wideband sparse RF signal with the binary iterative hard thresholding (BIHT) algorithm, which can alleviate the negative impact of the SNR degradation caused by the photonic link. A full theoretical framework of the PCS system with 1-bit quantization is given. Simulation results show that the PCS system with 1-bit quantization can provide better recovery performance than the traditional PCS system under low SNR and stringent bit budget.

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.