OBJECTIVE: To explore clinical characteristics, lesion sites and correlation differences of different types of diabetic foot gangrene, and to provide evidence-based basis for clinical classification of diabetic foot gangrene. METHODS: A retrospective analysis was conducted on 266 patients with newly diagnosed diabetic foot gangrene who were admitted from January 2018 to December 2018, including 183 males and 83 females, aged from 35 to 92 years old with an average of (69.55±10.84) years old, and they were divided into wet gangrene group and dry gangrene group according to the different natures of gangrene. There were 139 patients in wet gangrene group, including 98 males and 41 females, aged from 35 to 90 years old with an average of (68.95±10.93) years old. There were 127 patients in dry gangrene group, including 85 males and 42 females, aged from 38 to 92 years old with an average of (70.21±10.75) years old. Body mass index (BMI), waist-to-hip ratio (WHR), body temperature, skin temperature difference between the affected and healthy sides of the lower extremities, and Wagner grade between two groups were recorded to evaluate symptoms and signs. The white blood cell count (WBC), neutrophil percentage (NEUT%), and C-reactive protein (C-reactive protein), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), and interleukin-6 (IL-6) in peripheral blood between two groups were detected and compared to evaluate the infection status;the severity of diabetic peripheral neuropathy (DPN) was evaluated by using Toronto Clinical Scoring System (TCSS);the degree of pain in patients with diabetic foot gangrene was evaluated by numerical rating scale (NRS); ankle-brachial index (ABI) and popliteal artery blood flow velocity were used to evaluate the degree of arterial lesions. Spearman correlation analysis was used to analyze the correlations between gangrene TCSS, ABI and age, BMI, WHR, body temperature, calf skin temperature difference, WBC, NEUT%, CRP, ESR, PCT, IL-6, NRS, and Wagner classification indicators. RESULTS: The body temperature, skin temperature difference between the affected and healthy sides of the lower extremities, Wagner grade, WBC, NEUT%, CRP, ESR, PCT, IL-6, TCSS score, ABI, and popliteal artery blood flow velocity in wet gangrene group were higher than those in dry gangrene group (P<0.01), and BMI, WHR, and NRS score in dry gangrene group were higher than those in wet gangrene group;the differences were all statistically significant (P<0.01). The results of Spearman correlation analysis showed TCSS score of gangrene patients was correlated with body temperature (r=0.214), calf skin temperature difference (r=0.364), WBC (r=0.240), NEUT% (r=0.291), CRP (r=0.347), ESR (r=0.167), PCT (r=0.241), IL-6 (r=0.316), and popliteal fossa arterial blood flow velocity (r=0.261) and Wagner grade (r=0.273) were positively correlated, and the differences were statistically significant (P<0.01). ABI was negatively correlated with age (r=-0.183), BMI (r=-0.252), WHR (r=-0.288), and NRS score (r=-0.354), and the differences were statistically significant (P<0.01). CONCLUSION Diabetic foot gangrene is an extremely difficult and critical disease. Wet gangrene has a significant synergic effect with infection and neuropathy, while dry gangrene is closely related to vascular occlusion. The main contradiction of gangrene could be revealed through blood vessels, nerves and infection, providing evidence-based basis for the selection of debridement timing, anti-infection strategies and revascularization, with the aim of reducing the risk of amputation.
Humans ; Male ; Female ; Aged ; Middle Aged ; Diabetic Foot/diagnosis* ; Aged, 80 and over ; Adult ; Retrospective Studies ; Gangrene/physiopathology* ; C-Reactive Protein

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

The choice of biopsy method is critical in diagnosing prostate cancer (PCa). This retrospective cohort study compared systematic biopsy (SB) or cognitive fusion-targeted biopsy combined with SB (CB) in detecting PCa and clinically significant prostate cancer (csPCa). Data from 2572 men who underwent either SB or CB in Fudan University Shanghai Cancer Center (Shanghai, China) between January 2019 and December 2023 were analyzed. Propensity score matching (PSM) was used to balance baseline characteristics, and detection rates were compared before and after PSM. Subgroup analyses based on prostate-specific antigen (PSA) levels and Prostate Imaging-Reporting and Data System (PI-RADS) scores were performed. Primary and secondary outcomes were the detection rates of PCa and csPCa, respectively. Of 2572 men, 1778 were included in the PSM analysis. Before PSM, CB had higher detection rates for both PCa (62.9% vs 52.4%, odds ratio [OR]: 1.54, P < 0.001) and csPCa (54.9% vs 43.3%, OR: 1.60, P < 0.001) compared to SB. After PSM, CB remained superior in detecting PCa (63.1% vs 47.9%, OR: 1.86, P < 0.001) and csPCa (55.0% vs 38.2%, OR: 1.98, P < 0.001). In patients with PSA 4-12 ng ml -1 (>4 ng ml -1 and ≤12 ng ml -1 , which is also applicable to the following text), CB detected more PCa (59.8% vs 40.7%, OR: 2.17, P < 0.001) and csPCa (48.1% vs 27.7%, OR: 2.42, P < 0.001). CB also showed superior csPCa detection in those with PI-RADS 3 lesions (32.1% vs 18.0%, OR: 2.15, P = 0.038). Overall, CB significantly improves PCa and csPCa detection, especially in patients with PSA 4-12 ng ml -1 or PI-RADS 3 lesions.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Propensity Score ; Retrospective Studies ; Middle Aged ; Aged ; Image-Guided Biopsy/methods* ; Prostate-Specific Antigen/blood* ; Prostate/diagnostic imaging*

OBJECTIVES: To investigate the risk factors for the occurrence of cardiopulmonary dysfunction following ligation of hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants. METHODS: A retrospective collection of clinical data was conducted on preterm infants with a gestational age of <34 weeks who were admitted to the Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology from January 2018 to August 2024. These infants underwent hsPDA ligation after 1-2 courses of failed ibuprofen treatment. Based on the occurrence of blood pressure changes and oxygenation or ventilation failure postoperatively, the infants were divided into a cardiopulmonary dysfunction group (19 cases) and a non-cardiopulmonary dysfunction group (40 cases). Binary logistic regression analysis was performed to explore risk factors for postoperative cardiopulmonary dysfunction. RESULTS: Binary logistic regression analysis indicated that a faster average weight gain rate preoperatively and low levels of free triiodothyronine (FT₃) within one week before surgery were risk factors for cardiopulmonary dysfunction following hsPDA ligation (P<0.05). Receiver operating characteristic curve analysis showed that an average weight gain rate >11.45 g/(kg·d) and FT₃ levels <2.785 pmol/L within one week before surgery had predictive value for postoperative cardiopulmonary dysfunction (P<0.05). The combination of these two indicators provided the highest predictive value (P<0.05), with an area under the curve of 0.825, a sensitivity of 79%, and a specificity of 75%. CONCLUSIONS An average weight gain rate exceeding 11.45 g/(kg·d) and FT₃ levels below 2.785 pmol/L within one week before surgery are risk factors affecting cardiopulmonary function after hsPDA ligation. Preoperative assessment and intervention should be strengthened to reduce the risk of postoperative complications.
Humans ; Ductus Arteriosus, Patent/physiopathology* ; Risk Factors ; Female ; Infant, Newborn ; Male ; Retrospective Studies ; Infant, Premature ; Ligation/adverse effects* ; Hemodynamics ; Postoperative Complications/etiology* ; Logistic Models ; Lung Diseases/etiology*

OBJECTIVES: To summarize the clinical characteristics, diagnosis, and treatment outcomes of neonatal venous thrombosis. METHODS: A retrospective analysis was conducted on the clinical data of 11 neonates with venous thrombosis admitted to the Department of Neonatology of Anhui Children's Hospital from January 2019 to September 2024. The clinical characteristics, diagnostic approaches, treatments, and outcomes were analyzed. RESULTS: Among the 11 neonates diagnosed with venous thrombosis, 5 were male, and 6 were preterm infants, with a median gestational age of 35⁺⁶ weeks, birth weight of (2 322±1 069) g, and admission temperature of (36.6±0.4)°C. The median age at symptom onset was 6 days. Of the 11 cases, 8 limb venous thromboses and 1 portal vein thrombosis were confirmed by vascular ultrasound, and 2 cases of intracranial venous sinus thrombosis were confirmed by magnetic resonance imaging. Ten cases received low molecular weight heparin for anticoagulation, with a treatment duration of (24±15) days; 2 cases were treated with urokinase thrombolysis, and 4 cases received fresh frozen plasma transfusion. Thrombosis resolved in 7 cases before discharge. Partial resolution occurred in 2 cases before discharge (1 continued outpatient treatment until resolution and 1 resolved during follow-up). One case was transferred to another hospital after 1 day of treatment and was discharged after thrombosis reduction. No adverse reactions such as bleeding were observed. One neonate with cerebral infarction at admission did not receive heparin anticoagulation and was followed up as an outpatient. CONCLUSIONS Vascular ultrasound is the most commonly used diagnostic method for neonatal venous thrombosis. Heparin anticoagulation is the recommended treatment. The overall prognosis of neonatal venous thrombosis is favorable.
Humans ; Male ; Venous Thrombosis/drug therapy* ; Infant, Newborn ; Female ; Retrospective Studies

The catalytic activity of 3-mercaptopyruvate (3MP) sulfurtransferase (MPST) converts 3MP to hydrogen sulfide (H₂S). However, the regulatory mechanisms governing MPST and its impact on the brain remain largely unexplored. Our study reveals the neuroprotective role of endothelial MPST-generated H₂S, regulated by protein phosphatase 2A (PP2A). Bioinformatics analysis and RNA sequencing demonstrated that endothelial PP2A is associated with neurodegenerative disease pathways. Cerebral ischemic mice exhibited significant inactivation of endothelial PP2A, evidenced by the reduction of PP2Acα in the brain endothelium. Mice with endothelium-specific null PP2A (PP2A^EC-cKO) exhibited neuronal loss, cognitive dysfunction, and long-term potentiation deficits. Postnatal inactivation of endothelial PP2A also contributes to cognitive dysfunction and neuronal loss. However, regaining endothelial PP2A activity by overexpressing Ppp2ca rescued neuronal dysfunction. Mechanistically, PP2A deficiency is intricately linked to the MPST-H₂S signaling pathway. A robust reduction in endothelial MPST-dependent H₂S production followed PP2A deficiency. Exogenous H₂S treatment and AAV-mediated overexpression of MPST in brain endothelial cells significantly mitigated neuronal dysfunction in PP2A^EC-cKO mice. Furthermore, PP2A deficiency promotes an increase in calcium influx and calpain2 phosphorylation, subsequently leading to MPST degradation. The PP2A activator (FTY720) and MPST activator (3MP sodium) both remarkably restored endothelial MPST-dependent H₂S production, subsequently rescuing ischemia-induced neurological deficits. In conclusion, our study demonstrates that endothelial PP2A deficiency leads to MPST degradation by activating calpain2, thus damaging neuronal function.

The efficient,precise,and rapid detection of trace copper ions(Cu2+)is of paramount importance in the realms of food safety,environmental monitoring,and medical health.By ingeniously utilizing the chemical properties of benzotriazole compounds in copper protection,and introducing an electrochemical reduction strategy,a silver nanoparticle composite structure supported by a nickel foam substrate was developed as an active platform for surface-enhanced Raman scattering(SERS)detection.This platform employed benzotriazole-5-carboxylic acid(BTAC)as a specific SERS probe molecule to achieve sensitive analysis of metal ion concentrations.The detection mechanism revealed a highly selective coordination between the Cu2+and the nitrogen atom in the triazole ring of BTAC,triggering subtle structural changes in the triazole ring.This was manifested by a significant and quantifiable shift in the characteristic peak of the SERS spectrum(particularly at 1001 cm-1),with a maximum shift of up to 30 cm-1.This phenomenon not only addressed the issue of reproducibility in quantitative analysis caused by the non-uniformity of SERS substrate materials but also significantly broadened the application boundaries of SERS technology for trace metal ion detection.It enabled ultra-sensitive detection of Cu2+concentrations ranging from 1×10-6 to 1×10-11 mol/L,with a detection limit as low as 1×10-11 mol/L,significantly enhancing detection sensitivity and accuracy.This work provided a novel and efficient strategy for rapid detection of trace Cu2+and enriched the application potential of SERS technology in food safety,environmental monitoring and biomedical analysis.

Interferon stimulating factor STING, a transmembrane protein residing in the endoplasmic reticulum, is extensively involved in the sensing and transduction of intracellular signals and serves as a crucial component of the innate immune system. STING is capable of directly or indirectly responding to abnormal DNA originating from diverse sources within the cytoplasm, thereby fulfilling its classical antiviral and antitumor functions. Structurally, STING is composed of 4 transmembrane helices, a cytoplasmic ligand binding domain (LBD), and a C terminal tail structure (CTT). The transmembrane domain (TM), which is formed by the transmembrane helical structures, anchors STING to the endoplasmic reticulum, while the LBD is in charge of binding to cyclic dinucleotides (CDNs). The classical second messenger, cyclic guanosine monophosphate-adenosine monophosphate (cGAMP), represents a key upstream molecule for STING activation. Once cGAMP binds to LBD, STING experiences conformational alterations, which subsequently lead to the recruitment of Tank-binding kinase 1 (TBK1) via the CTT domain. This, in turn, mediates interferon secretion and promotes the activation and migration of dendritic cells, T cells, and natural killer cells. Additionally, STING is able to activate nuclear factor-κB (NF-κB), thereby initiating the synthesis and release of inflammatory factors and augmenting the body’s immune response. In recent years, an increasing number of studies have disclosed the non-classical functions of STING. It has been found that STING plays a significant role in organelle regulation. STING is not only implicated in the quality control systems of organelles such as mitochondria and endoplasmic reticulum but also modulates the functions of these organelles. For instance, STING can influence key aspects of organelle quality control, including mitochondrial fission and fusion, mitophagy, and endoplasmic reticulum stress. This regulatory effect is not unidirectional; rather, it is subject to organelle feedback regulation, thereby forming a complex interaction network. STING also exerts a monitoring function on the nucleus and ribosomes, which further enhances the role of the cGAS-STING pathway in infection-related immunity. The interaction mechanism between STING and organelles is highly intricate, which, within a certain range, enhances the cells’ capacity to respond to external stimuli and survival pressure. However, once the balance of this interaction is disrupted, it may result in the occurrence and development of inflammatory diseases, such as aseptic inflammation and autoimmune diseases. Excessive activation or malfunction of STING may trigger an over-exuberant inflammatory response, which subsequently leads to tissue damage and pathological states. This review recapitulates the recent interactions between STING and diverse organelles, encompassing its multifarious functions in antiviral, antitumor, organelle regulation, and immune regulation. These investigations not only deepen the comprehension of molecular mechanisms underlying STING but also offer novel concepts for the exploration of human disease pathogenesis and the development of potential treatment strategies. In the future, with further probing into STING function and its regulatory mechanisms, it is anticipated to pioneer new approaches for the treatment of complex diseases such as inflammatory diseases and tumors.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.