Infectious keratitis (IK) is a leading cause of blindness worldwide, primarily resulting from improper contact lens use, trauma, and a compromised immune response. The pathogenic microorganisms responsible for IK include bacteria, fungi, viruses, and Acanthamoeba. This review examines standard therapeutic agents for treating IK, including broad-spectrum empiric antibiotics for bacterial keratitis (BK), antifungals such as voriconazole and natamycin for fungal infections, and antiviral nucleoside analogues for viral keratitis (VK). Additionally, this review discusses therapeutic agents, such as polyhexamethylene biguanide (PHMB), for the treatment of Acanthamoeba keratitis (AK). The review also addresses emerging drugs and the challenges associated with their clinical application, including anti-biofilm agents that combat drug resistance and nuclear factor kappa-B (NF-κB) pathway-targeted therapies to mitigate inflammation. Furthermore, methods of Photodynamic Antimicrobial Therapy (PDAT) are explored. This review underscores the importance of integrating novel and traditional therapies to tackle drug resistance and enhance drug delivery, with the goal of advancing treatment strategies for IK.

Objective To analyze the effect of Hsa-circ-0101216 on gemcitabine(GEM)chemotherapy resistance in pancreatic cancer and its mechanism.Methods Differentially expressed circRNAs between GEM-resistant pancreatic cancer cells and parent cells were screened using the GEO database.Pancreatic cancer GEM resistant cell lines(BxPC-3-GEM and Capan-1-GEM)were constructed by intermittent concentration gradient method.qRT-PCR was used to detect the expression of Hsa-circ-0101216 in cells.GEM resistant pancreatic cancer cell lines were taken and divided into sh-circ-0101216 group(knockdown of circ-0101216),sh-NC group(transfected with sh-NC),and blank control group(untreated).CCK-8 assay and EdU proliferation assay were used to detect the half inhibitory concentration(IC50)of GEM and proliferation ability of cells in each group.Western blotting was performed to detect the expression of multidrug resistance-related protein 1(MRP1),breast cancer resistance protein(BCRP),and human equilibrative nucleoside transporter-1(hENT-1).A subcutaneous xenograft tumor model of human pancreatic cancer in nude mice was constructed,and sh-NC+GEM group and sh-circ-0101216+GEM group(n=6)were set up.The volume and weight of xenograft tumor in nude mice were compared between the two groups.Western blotting and immunohistochemistry were used to detect the expression of MRP1,BCRP,and hENT-1 proteins in xenograft tumor tissues,and EDU proliferation assay was used to detect the proliferation ability of tumor cells.Results The GEO database screening showed that Hsa-circ-0101216 was up-regulated in GEM-resistant pancreatic cancer cell lines.Pancreatic cancer GEM-resistant cell lines were successfully constructed,and the expression levels of Hsa-circ-0101216 and the IC50 value in GEM-resistant pancreatic cancer cells BxPC-3-GEM and Capan-1-GEM were significantly higher than those in parental cells(P<0.05).In sh-circ-0101216 group,the IC50 values of GEM,cell viability,EdU positivity rate,and the expression levels of MRP1 and BCRP proteins in GEM-resistant pancreatic cancer cells BxPC-3-GEM and Capan-1-GEM were significantly lower than those in blank control group and sh-NC group,while the expression level of hENT-1 protein was significantly higher(P<0.05 or P<0.001).In sh-circ-0101216+GEM group,the weight and volume of subcutaneous xenograft tumors in nude mice,the expression levels and positive expression rates of MRP1 and BCRP proteins in tumor tissues,and the EdU positive rate were significantly lower than those in sh-NC+GEM group,while the expression level and positive expression rate of hENT-1 protein were significantly higher(P<0.05).Conclusions Hsa-circ-0101216 is highly expressed in GEM-resistant pancreatic cancer cell lines.Its knockdown can inhibit the proliferation of pancreatic cancer cells and enhance the chemosensitivity of pancreatic cancer cells to GEM.The mechanism may be related to the regulation of transmembrane transporter protein expression.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

This article discusses the revision background and core content of 9621 General Requirements for Phar-maceutical Packaging Materials and Containers in the Chinese Pharmacopoeia 2025 Edition.In combination with the requirements of bundling review of pharmaceutical packaging and drug products,as well as harmonization of inter-national standards,it analyzed the revision philosophy of the new guideline and the impact to industry.The new edition of the guideline 9621 introduces the concept of "pharmaceutical packaging system" for the first time,constructing four suitability evaluation dimensions covering protection,compatibility,safety,and functionality,it also introduces the requirements for the self-stability evaluation of plastic and rubber pharmaceutical packaging materials,and strengthening the concept of full lifecycle management and risk management.Through the"1+4+58" standard system,the basis for determining the key quality attributes of packaging materials for phar-maceutical use and the optimization path of dynamic inspection rules have been clarified.This revision has resolved the contradiction between the old version of standards and current regulatory requirements through a systematic upgrade,promoting the transformation of pharmaceutical packaging materials and containers quality control towards a scientific,international,and full lifecycle management model,proving technical supports in ensuring the safety,effectiveness of drug products from standard perspective,including advancing the industry high-quality development.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Liver diseases, particularly cirrhosis and cancer, significantly threat human health for a long time, and their diagnosis and treatment are important topics in medical research. Tradi-tional diagnostic and therapeutic approaches have focused on biochemical properties of liver diseases, yet often neglecting the mechanical microenvironment of liver at molecular, cellular, and tissue levels. This oversight makes it difficult to meet clinical needs. Recent advancements in biomechanics and mechanobiology have underscored the importance of mechanical properties of liver in understanding disease mechanisms, presenting profound implications for both basic research and clinical practice. However, there is still a lack of in-depth exploration of the mechanical properties of liver in both basic research and clinical treatment, making it unclear of the specific mechanisms and application scenarios. The authors propose and introduce the emerging field of liver mechanomedicine, examine the biomechanical properties of liver and their alterations during disease progression, elucidate mechanobiological mechanisms of cellular mechanical responses and signal transduction, explore the application of mechanical characteristics in the diagnosis and treatment of liver diseases, aiming to provide a new perspective for basic research and clinical practice.

Liver diseases, particularly cirrhosis and cancer, significantly threat human health for a long time, and their diagnosis and treatment are important topics in medical research. Tradi-tional diagnostic and therapeutic approaches have focused on biochemical properties of liver diseases, yet often neglecting the mechanical microenvironment of liver at molecular, cellular, and tissue levels. This oversight makes it difficult to meet clinical needs. Recent advancements in biomechanics and mechanobiology have underscored the importance of mechanical properties of liver in understanding disease mechanisms, presenting profound implications for both basic research and clinical practice. However, there is still a lack of in-depth exploration of the mechanical properties of liver in both basic research and clinical treatment, making it unclear of the specific mechanisms and application scenarios. The authors propose and introduce the emerging field of liver mechanomedicine, examine the biomechanical properties of liver and their alterations during disease progression, elucidate mechanobiological mechanisms of cellular mechanical responses and signal transduction, explore the application of mechanical characteristics in the diagnosis and treatment of liver diseases, aiming to provide a new perspective for basic research and clinical practice.