Membralin is required for maize development and defines a branch of the endoplasmic reticulum-associated degradation pathway in plants.

Endoplasmic reticulum (ER)-associated degradation (ERAD) plays key roles in controlling protein levels and quality in eukaryotes. The Ring Finger Protein 185 (RNF185)/membralin ubiquitin ligase complex was recently identified as a branch in mammals and is essential for neuronal function, but its function in plant development is unknown. Here, we report the map-based cloning and characterization of Narrow Leaf and Dwarfism 1 (NLD1), which encodes the ER membrane-localized protein membralin and specifically interacts with maize homologs of RNF185 and related components. The nld1 mutant shows defective leaf and root development due to reduced cell number. The defects of nld1 were largely restored by expressing membralin genes from Arabidopsis thaliana and mice, highlighting the conserved roles of membralin proteins in animals and plants. The excessive accumulation of ß-hydroxy ß-methylglutaryl-CoA reductase in nld1 indicates that the enzyme is a membralin-mediated ERAD target. The activation of bZIP60 mRNA splicing-related unfolded protein response signaling and marker gene expression in nld1, as well as DNA fragment and cell viability assays, indicate that membralin deficiency induces ER stress and cell death in maize, thereby affecting organogenesis. Our findings uncover the conserved, indispensable role of the membralin-mediated branch of the ERAD pathway in plants. In addition, ZmNLD1 contributes to plant architecture in a dose-dependent manner, which can serve as a potential target for genetic engineering to shape ideal plant architecture, thereby enhancing high-density maize yields.

Flexible Substrate-Compatible and Efficiency-Improved Quantum-Dot Light-Emitting Diodes with Reduced Annealing Temperature of NiOx Hole-Injecting Layer.

The growing demand for wearable and attachable displays has sparked significant interest in flexible quantum-dot light-emitting diodes (QLEDs). However, the challenges of fabricating and operating QLEDs on flexible substrates persist due to the lack of stable and low-temperature processable charge-injection/-transporting layers with aligned energy levels. In this study, we utilized NiOx nanoparticles that are compatible with flexible substrates as a hole-injection layer (HIL). To enhance the work function of the NiOx HIL, we introduced a self-assembled dipole modifier called 4-(trifluoromethyl)benzoic acid (4-CF3-BA) onto the surface of the NiOx nanoparticles. The incorporation of the dipole molecules through adsorption treatment has significantly changed the wettability and electronic characteristics of NiOx nanoparticles, resulting in the formation of NiO(OH) at the interface and a shift in vacuum level. The alteration of surface electronic states of the NiOx nanoparticles not only improves the carrier balance by reducing the hole injection barrier but also prevents exciton quenching by passivating defects in the film. Consequently, the NiOx-based red QLEDs with interfacial modification demonstrate a maximum current efficiency of 16.1 cd/A and a peak external quantum efficiency of 10.3%. This represents a nearly twofold efficiency enhancement compared to control devices. The mild fabrication requirements and low annealing temperatures suggest potential applications of dipole molecule-modified NiOx nanoparticles in flexible optoelectronic devices.

GABA-mediated inhibition of human CD4+ T cell functions is enhanced by insulin but impaired by high glucose levels.

BACKGROUND: γ-aminobutyric acid (GABA), known as the main inhibitory neurotransmitter in the brain, exerts immunomodulatory functions by interaction with immune cells, including T cells. Metabolic programs of T cells are closely linked to their effector functions including proliferation, differentiation, and cytokine production. The physiological molecules glucose and insulin may provide environmental cues and guidance, but whether they coordinate to regulate GABA-mediated T cell immunomodulation is still being examined. METHODS: CD4+ T cells that were isolated from blood samples from healthy individuals and from patients with type 1 diabetes (T1D) were activated in vitro. We carried out metabolic assays, multiple proximity extension assay (PEA), ELISA, qPCR, immunoblotting, immunofluorescence staining, flow cytometry analysis, MS-based proteomics, as well as electrophysiology and live-cell Ca2+ imaging. FINDINGS: We demonstrate that GABA-mediated reduction of metabolic activity and the release of inflammatory proteins, including IFNγ and IL-10, were abolished in human CD4+ T cells from healthy individuals and patients with T1D when the glucose concentration was elevated above levels typically observed in healthy people. Insulin increased GABAA receptor-subunit ρ2 expression, enhanced the GABAA receptors-mediated currents and Ca2+ influx. GABA decreased, whereas insulin sustained, hexokinase activity and glycolysis in a glucose concentration-dependent manner. INTERPRETATION: These findings support that metabolic factors, such as glucose and insulin, influence the GABA-mediated immunomodulation of human primary T cells effector functions. FUNDING: The Swedish Children's Diabetes Foundation, The Swedish Diabetes Foundation, The Swedish Research Council 2018-02952, EXODIAB, The Ernfors Foundation, The Thurings Foundation and the Science for Life Laboratory.

Single-Cell Transcriptomic Profiling Unveils Dynamic Immune Cell Responses during Haemonchus contortus Infection.

BACKGROUND: Haemonchus contortus is a parasite widely distributed in tropical, subtropical, and warm temperate regions, causing significant economic losses in the livestock industry worldwide. However, little is known about the genetics of H. contortus resistance in livestock. In this study, we monitor the dynamic immune cell responses in diverse peripheral blood mononuclear cells (PBMCs) during H. contortus infection in goats through single-cell RNA sequencing (scRNA-Seq) analysis. METHODS AND RESULTS: A total of four Boer goats, two goats with oral infection with the L3 larvae of H. contortus and two healthy goats as controls, were used in the animal test. The infection model in goats was established and validated by the fecal egg count (FEC) test and qPCR analysis of the gene expression of IL-5 and IL-6. Using scRNA-Seq, we identified seven cell types, including T cells, monocytes, natural killer cells, B cells, and dendritic cells with distinct gene expression signatures. After identifying cell subpopulations of differentially expressed genes (DEGs) in the case and control groups, we observed the upregulation of multiple inflammation-associated genes, including NFKBIA and NFKBID. Kyoto Encyclopedia of the Genome (KEGG) enrichment analysis revealed significant enrichment of NOD-like receptor pathways and Th1/Th2 cell differentiation signaling pathways in CD4 T cells DEGs. Furthermore, the analysis of ligand-receptor interaction networks showed a more active state of cellular communication in the PBMCs from the case group, and the inflammatory response associated MIF-(CD74 + CXCR4) ligand receptor complex was significantly more activated in the case group, suggesting a potential inflammatory response. CONCLUSIONS: Our study preliminarily revealed transcriptomic profiling characterizing the cell type specific mechanisms in host PBMCs at the single-cell level during H. contortus infection.

Effects of Environmental Organic Pollutants on Environment and Human Health: The Latest Updates.

This editorial introduces the Special Issue "Effects of Environmental Organic Pollutants on Environment and Human Health: The Latest Updates" [...].

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS: Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS: Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

pGM-CSF as an adjuvant in DNA vaccination against SARS-CoV-2.

The emergence of SARS-CoV-2 presents a significant global public health dilemma. Vaccination has long been recognized as the most effective means of preventing the spread of infectious diseases. DNA vaccines have attracted attention due to their safety profile, cost-effectiveness, and ease of production. This study aims to assess the efficacy of plasmid-encoding GM-CSF (pGM-CSF) as an adjuvant to augment the specific humoral and cellular immune response elicited by DNA vaccines based on the receptor-binding domain (RBD) antigen. Compared to the use of plasmid-encoded RBD (pRBD) alone, mice that were immunized with a combination of pRBD and pGM-CSF exhibited significantly elevated levels of RBD-specific antibody titers in serum, BALF, and nasal wash. Furthermore, these mice generated more potent neutralization antibodies against both the wild-type and Omicron pseudovirus, as well as the ancestral virus. In addition, pGM-CSF enhanced pRBD-induced CD4+ and CD8+ T cell responses and promoted central memory T cells storage in the spleen. At the same time, tissue-resident memory T (Trm) cells in the lung also increased significantly, and higher levels of specific responses were maintained 60 days post the final immunization. pGM-CSF may play an adjuvant role by promoting antigen expression, immune cells recruitment and GC B cell responses. In conclusion, pGM-CSF may be an effective adjuvant candidate for the DNA vaccines against SARS-CoV-2.

Cationic Nanoemulsion-Encapsulated Retinoic Acid as an Adjuvant to Promote OVA-Specific Systemic and Mucosal Responses.

Cationic nanostructures have emerged as an adjuvant and antigen delivery system that enhances dendritic cell maturation, ROS generation, and antigen uptake and then promotes antigen-specific immune responses. In recent years, retinoic acid (RA) has received increasing attention due to its effect in activating the mucosal immune response; however, in order to use RA as a mucosal adjuvant, it is necessary to solve the problem of its dissolution, loading, and delivery. Here, we describe a cationic nanoemulsion-encapsulated retinoic acid (CNE-RA) delivery system composed of the cationic lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOTAP), retinoic acid, squalene as the oil phase, polysorbate 80 as surfactant, and sorbitan trioleate 85 as co-surfactant. Its physical and chemical properties were characterized using dynamic light scattering and a spectrophotometer. Immunization of mice with the mixture of antigen (ovalbumin, OVA) and CNE-RA significantly elevated the levels of anti-OVA secretory immunoglobulin A (sIgA) in vaginal lavage fluid and the small intestinal lavage fluid of mice compared with OVA alone. This protocol describes a detailed method for the preparation, characterization, and evaluation of the adjuvant effect of CNE-RA.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Hi-Tag: a simple and efficient method for identifying protein-mediated long-range chromatin interactions with low cell numbers.

Protein-mediated chromatin interactions can be revealed by coupling proximity-based ligation with chromatin immunoprecipitation. However, these techniques require complex experimental procedures and millions of cells per experiment, which limits their widespread application in life science research. Here, we develop a novel method, Hi-Tag, that identifies high-resolution, long-range chromatin interactions through transposase tagmentation and chromatin proximity ligation (with a phosphorothioate-modified linker). Hi-Tag can be implemented using as few as 100,000 cells, involving simple experimental procedures that can be completed within 1.5 days. Meanwhile, Hi-Tag is capable of using its own data to identify the binding sites of specific proteins, based on which, it can acquire accurate interaction information. Our results suggest that Hi-Tag has great potential for advancing chromatin interaction studies, particularly in the context of limited cell availability.

Correlation of morning dry mouth with clinical features of OSA in a community population: a cross-sectional study.

OBJECTIVES: Morning dry mouth, commonly seen in Obstructive Sleep Apnea (OSA) patients, is absent in current OSA screening tools. This study evaluated the link between morning dry mouth and OSA's clinical symptoms and complications, aiming to determine its viability as a screening indicator. METHODS: This research analyses baseline data from a prospective cohort study (the PIFCOPD study). Demographic information, medical history, and the presence of morning dry mouth symptoms were collected. The STOP-Bang questionnaire was performed for OSA screening. Logistic regression analyses were employed to establish the correlations between morning dry mouth and the clinical symptoms and comorbidities of OSA. RESULT: 1291 participants (62.1±7.5 years; 501 males, 790 females) were included, of which 416 reported morning dry mouth (32.2%). 42.6% in the high-risk OSA group and 22.1% in the low-risk group reported morning dry mouth. Individuals with morning dry mouth also showed higher STOP-Bang scores (3.3±1.6 vs. 2.3±1.4, P<0.01). Significant associations were found between morning dry mouth and loud snoring, observed sleep apnea, daytime fatigue, and hyperlipidemia (P<0.01), but not with alcohol consumption, tea consumption, diabetes, or hypertension. CONCLUSION: Morning dry mouth is associated with increased OSA risk and its clinical signs, suggesting its potential as an OSA screening symptom. CLINICAL TRIAL REGISTRATION: This study has been registered at www.ClinicalTrials.gov (registration identifier: NCT03532893) on 21 May 2018.

Mesenteric adipose tissue B lymphocytes promote intestinal injury in severe acute pancreatitis by mediating enteric pyroptosis.

BACKGROUND: Visceral adipose tissue (VAT) has been linked to the severe acute pancreatitis (SAP) prognosis, although the underlying mechanism remains unclear. It has been reported that pyroptosis worsens SAP. The present study aimed to verify whether mesenteric adipose tissue (MAT, a component of VAT) can cause secondary intestinal injury through the pyroptotic pathway. METHODS: Thirty-six male Sprague Dawley (SD) rats were divided into six different groups. Twelve rats were randomly divided into the SAP and control groups. We monitored the changes of MAT and B lymphocytes infiltration in MAT of SAP rats. Twelve SAP rats were injected with MAT B lymphocytes or phosphate buffer solution (PBS). The remaining twelve SAP rats were first injected with MAT B lymphocytes, and then with MCC950 (NLRP3 inhibitor) or PBS. We collected blood and tissue samples from pancreas, gut and MAT for analysis. RESULTS: Compared to the control rats, the SAP group showed inflammation in MAT, including higher expression of tumor necrosis factor (TNF-α) and interleukin-6 (IL-6), lower expression of IL-10, and histological changes. Flow cytometry analysis revealed B lymphocytes infiltration in MAT but not T lymphocytes and macrophages. The SAP rats also exhibited intestinal injury, characterized by lower expression of zonula occludens-1 (ZO-1) and occludin, higher levels of lipopolysaccharide and diamine oxidase, and pathological changes. The expression of NLRP3 and n-GSDMD, which are responsible for pyroptosis, was increased in the intestine of SAP rats. The injection of MAT B lymphocytes into SAP rats exacerbated the inflammation in MAT. The upregulation of pyroptosis reduced tight junction in the intestine, which contributed to the SAP progression, including higher inflammatory indicators and worse histological changes. The administration of MCC950 to SAP + MAT B rats downregulated pyroptosis, which subsequently improved the intestinal barrier and ameliorated inflammatory response of SAP. CONCLUSIONS: In SAP, MAT B lymphocytes aggravated local inflammation, and promoted the injury to the intestine through the enteric pyroptotic pathway.

Akkermansia muciniphila-derived small extracellular vesicles attenuate intestinal ischemia-reperfusion-induced postoperative cognitive dysfunction by suppressing microglia activation via the TLR2/4 signaling.

Akkermansia muciniphila (AKK) bacteria improve the functions of theere intestinal and blood-brain barriers (BBB) via their extracellular vesicles (AmEvs). However, their role in postoperative cognitive dysfunction (POCD) and its underlying mechanisms remain unclear. To investigate, we used C57BL/6 J mice divided into five groups: Sham, POCD, POCD+Akk, POCD+Evs, and POCD+Evs + PLX5622. POCD was induced through intestinal ischemia-reperfusion (I/R). The mice's cognitive function was assessed using behavioral tests, and possible mechanisms were explored by examining gut and BBB permeability, inflammation, and microglial function. Toll-like receptor (TLR) 2/4 pathway-related proteins were also investigated both in vitro and in vivo. PLX5622 chow was employed to eliminate microglial cells. Our findings revealed a negative correlation between AKK abundance and POCD symptoms. Supplementation with either AKK or AmEvs improved cognitive function, improved the performance of the intestinal barrier and BBB, and decreased inflammation and microglial activation in POCD mice compared to controls. Moreover, AmEvs treatment inhibited TLR2/4 signaling in the brains of POCD mice and LPS-treated microglial cells. In microglial-ablated POCD mice, however, AmEvs failed to protect BBB integrity. Overall, AmEvs is a potential therapeutic strategy for managing POCD by enhancing gut and BBB integrity and inhibiting microglial-mediated TLR2/4 signaling.

Sphenomandibular ligament and degenerating Meckel's cartilage revisited: Sequential variations with temporal bone deformity for ligament attachment in near-term human fetuses.

BACKGROUND: The sphenomandibular ligament (SML) is considered to originate from Meckel's cartilage (MC). However, no study has examined how the os goniale contributes to SML development. METHODS: Semiserial histological sections of heads from 18 near-term fetuses at 27-40 weeks of gestation were examined. OBSERVATIONS: The os goniale and the anterior process of the malleus (AP) provided a long, bar-like membranous bone complex that passed through the petrotympanic and tympanosquamosal fissures. Notably, the AP-goniale complex is sometimes elongated inferiorly to join the SML (n = 4 specimens). Along the complex in the bone fissures, a degenerating MC was often present (n = 12). With (n = 6) or without (n = 3) the MC remnant, the tympanic bone (TYB) protruded inferomedially near the tympanosquamosal fissure, and it sometimes continued to a cartilaginous SML (n = 3). The temporal bone squamosa or petrosa provided a similar bony process approaching the SML. The middle meningeal artery often ran between the sphenoid and petrosa. CONCLUSIONS: Most of the specimens (n = 15) exhibited a sequential change from a cartilaginous SML as a continuation of the MC remnant to the ligament after the disappearance of the cartilage. The degenerating MC appeared to cause transformation from the AP-goniale complex and/or TYB to "another ligament" that replaced the usual SML at the upper part. Near the MC remnant, a similar transformation was also suggested on the squamosa or petrosa. The sphenoid spine appeared to originate often from the sphenoid ala major but sometimes from the TYB.

3D hepatic organoid production from human pluripotent stem cells.

Hepatic organoids might provide a golden opportunity for realizing precision medicine in various hepatic diseases. Previously described hepatic organoid protocols from pluripotent stem cells rely on complicated multiple differentiation steps consisting of both 2D and 3D differentiation procedures. Therefore, the spontaneous formation of hepatic organoids from 2D monolayer culture is associated with a low-throughput production, which might hinder the standardization of hepatic organoid production and hamper the translation of this technology to the clinical or industrial setting. Here we describe the stepwise and fully 3D production of hepatic organoids from human pluripotent stem cells. We optimized every differentiation step by screening for optimal concentrations and timing of differentiation signals in each differentiation step. Hepatic organoids are stably expandable without losing their hepatic functionality. Moreover, upon treatment of drugs with known hepatotoxicity, we found hepatic organoids are more sensitive to drug-induced hepatotoxicity compared with 2D hepatocytes differentiated from PSCs, making them highly suitable for in vitro toxicity screening of drug candidates. The standardized fully 3D protocol described in the current study for producing functional hepatic organoids might serve as a novel platform for the industrial and clinical translation of hepatic organoid technology.

Association between adjuvant chemotherapy and survival in stage I gastric cancer patients after curative resection.

Background: The efficacy of adjuvant chemotherapy (AC) on survival outcomes of patients with stage I gastric cancer (GC) after curative resection remains controversial. We aimed to determine whether these patients would benefit from AC. Methods: This retrospective study included patients with pathologically confirmed stage I GC who underwent curative resection between November 2010 and December 2020. Patients were divided into AC and non-AC groups, then a 1:1 propensity score matching (PSM) analysis was performed to minimize the selection bias. Potential risk factors including age, pN stage, pT stage, lymphovascular invasion, perineural invasion, tumor size, histological type, and carcinoembryonic antigen level were used as matching covariates. The recurrence-free survival (RFS) and disease-specific survival (DSS) were compared between groups using the Kaplan-Meier method. Results: A total of 902 consecutive patients were enrolled and 174 (19.3%) patients were treated with AC. PSM created 123 pairs of patients. Before PSM, patients receiving AC had lower 10-year RFS rates (90% vs 94.6%, P = 0.035) than those who did not receive AC; the two groups had similar 10-year DSS rates (93.8% vs 95.0%, P = 0.240). After PSM, there were no statistical differences in the 10-year RFS (90.9% vs 93.0%, P = 0.507) or DSS rates (93.5% vs 93.6%, P = 0.811) between the two groups. Similar results were found in the stage IA and IB subgroups. Moreover, these findings were not affected by AC cycles. Conclusions: The addition of AC could not provide survival benefits for patients with stage I GC after surgery and follow-up is thus recommended. However, large-scale randomized clinical trials are required.

Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study.

Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.