Purpose: This study aimed to evaluate the safety and efficacy of gemcitabine and cisplatin (GP) regimen in combination with immune checkpoint inhibitor sintilimab as neoadjuvant therapy for muscle-invasive bladder cancer (MIBC) patients and the feasibility of the following selective bladder sparing surgery. Materials and Methods: Patients with histopathologically confirmed urothelial carcinoma without distant metastases (T2-4a, N ≤ 1, M0, American Joint Committee of Cancer 8th) and with adequate organ function will be enrolled. The therapeutic regimen was sintilimab 200 mg once on day 8, gemcitabine 1,000 mg/m2 and cisplatin 35 mg/m2 once on days 1 and 8, every 21 days for four cycles. The primary endpoint was pathologic complete response (pCR, pT0N0) rate. The secondary end points were ypT < 2 rate, R0 resection rate, event-free survival, and safety. Results: From May 4, 2020, to May 20, 2023, 55 patients were enrolled. Forty-six patients were evaluated for efficacy. Among the 42 patients who underwent surgery, 16 patients (38.0%) achieved pCR. Thirty-three patients (78.6%) achieved pT < 2. With a median follow-up of 15.7 months, the 1-year event-free survival was 91.3%. Notwithstanding the poor pathological baseline characteristic of a high T3-T4a proportion (39.1%), a promising bladder preservation (including 22 patients transurethral resection of bladder tumor, 5 patients partial cystectomy, and 4 surveillances) rate was achieved (67.4%). The most common grade ≥ 3 treatment-related adverse events was neutropenia (n=15, 27.3%), which was related to chemotherapy. There were no grade 3 immune-related adverse events. Conclusion Neoadjuvant GP plus sintilimab is a promising regimen for MIBC patients, with relatively high pT < 2 rate and triggering the emerging roles for the multi-disciplinary team decision-making for bladder sparing surgery.

Objective Jejunal interposition is an important option for restoring gastrointestinal continuity in patients with complex esophageal cancer in whom the stomach is unavailable for reconstruction due to previous gastrectomy, tumor involvement, failed gastric conduit, or other constraints. Although the jejunum offers a caliber similar to the esophagus, a rich blood supply, and preserved peristaltic function, anatomical variations in mesenteric length and vascular arcades render long-segment and high-level reconstruction susceptible to excessive tension, mesenteric torsion, venous congestion, and inadequate distal perfusion. This consensus aims to provide standardized recommendations for the application of jejunal interposition in this specific patient population. Methods This consensus was developed in accordance with the WHO Handbook for Guideline Development and Chinese national guideline development standards, with reference to the Appraisal of Guidelines for Research & Evaluation Ⅱ(AGREE Ⅱ), Reporting Items for Practice Guidelines in Healthcare (RIGHT), and Scientific, Transparent and Applicable Rankings (STAR) checklists. The project was registered on the international practice guidelines registry platform (PREPARE-2026CN1384). The working group systematically searched Chinese and English evidence addressing 12 clinical questions. Evidence was graded using the Oxford Centre for Evidence-Based Medicine (OCEBM) 2009 system, with structured appraisal of risk of bias, inconsistency, indirectness, and imprecision. Recommendations were formulated through two rounds of Delphi surveys and a face-to-face consensus meeting, with an agreement threshold of ≥75%. Results The consensus generated 12 recommendations covering indications, selection between jejunum and colon, preoperative assessment, surgical approach and reconstruction route, pedicled jejunal interposition (PJI), supercharged pedicled jejunal interposition (SPJ), indocyanine green (ICG) perfusion assessment, perioperative nutrition, management of anastomotic leakage, long-term follow-up, and special treatment scenarios. Given that the available direct evidence is predominantly derived from single-center retrospective cohorts and case series, recommendations extrapolated from general esophagectomy or other gastrointestinal surgical evidence are deliberately phrased with caution. Conclusion Jejunal interposition is a safe and effective approach for digestive tract reconstruction in patients with complex esophageal cancer in whom the stomach is unavailable. This consensus emphasizes that clinical decision-making should be individualized based on the patient's prior treatment history, reconstruction height, vascular anatomy, technical availability, and institutional experience in the Chinese context.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

The polymerase 1 and transcript release factor (PTRF)-cytoplasmic phospholipase A2 (cPLA2) phospholipid remodeling pathway facilitates tumor proliferation in glioma. Nevertheless, blockade of this pathway leads to the excessive activation of oncogenic receptors on the plasma membrane and subsequent drug resistance. Here, CD26/dipeptidyl peptidase 4 (DPP4) was identified through screening of CRISPR/Cas9 libraries. Suppressing PTRF-cPLA2 signaling resulted in the activation of the epidermal growth factor receptor (EGFR) pathway through phosphatidylcholine and lysophosphatidylcholine remodeling, which ultimately increased DPP4 transcription. In turn, DPP4 interacted with EGFR and prevented its ubiquitination. Linagliptin, a DPP4 inhibitor, facilitated the degradation of EGFR by blocking its interaction with DPP4. When combined with the cPLA2 inhibitor AACOCF3, it exhibited synergistic effects and led to a decrease in energy metabolism in glioblastoma cells. Subsequent in vivo investigations provided further evidence of a synergistic impact of linagliptin by augmenting the sensitivity of AACOCF3 and strengthening the efficacy of temozolomide. DPP4 serves as a novel target and establishes a constructive feedback loop with EGFR. Linagliptin is a potent inhibitor that promotes EGFR degradation by blocking the DPP4-EGFR interaction. This study presents innovative approaches for treating glioma by combining linagliptin with AACOCF3 and temozolomide.

Exosomal circular RNA (circRNAs) are pivotal in cancer biology, and tumor pathophysiology. These stable, non-coding RNAs encapsulated in exosomes participated in cancer progression, tumor growth, metastasis, drug sensitivity and the tumor microenvironment (TME). Their presence in bodily fluids positions them as potential non-invasive biomarkers, revealing the molecular dynamics of cancers. Research in exosomal circRNAs is reshaping our understanding of neoplastic intercellular communication. Exploiting the natural properties of exosomes for targeted drug delivery and disrupting circRNA-mediated pro-tumorigenic signaling can develop new treatment modalities. Therefore, ongoing exploration of exosomal circRNAs in cancer research is poised to revolutionize clinical management of cancer. This emerging field offers hope for significant breakthroughs in cancer care. This review underscores the critical role of exosomal circRNAs in cancer biology and drug resistance, highlighting their potential as non-invasive biomarkers and therapeutic targets that could transform the clinical management of cancer.

OBJECTIVE: To investigate whether the presence or absence of improvement in chronic severe functional constipation (CSFC) at the early stage of treatment with electroacupuncture predicts subsequent response or non-response, and to determine the optimal treatment duration for assessing subsequent responses to electroacupuncture. METHODS: This is a post hoc analysis using data pooled from two large-scale randomized controlled trials. Patients with CSFC were recruited, and those in the electroacupuncture groups were included in the present study. Early improvement was defined as a weekly increase of ≥1 complete spontaneous bowel movement (CSBM) compared to baseline. Three treatment response criteria were evaluated: ≥ 3 CSBMs per week, overall CSBM response, and sustained CSBM response. Predictive statistics, including sensitivity, specificity, positive predictive value, and negative predictive value, were calculated at weeks 1-4. Receiver operating characteristic curves and accuracy rates were used to determine the optimal timepoint for differentiation between responders and non-responders. RESULTS: Cases from a total of 813 participants who received electroacupuncture were analyzed. The proportion of improvers was 40.34% by week 1, increasing to 52.52% by week 4. After 8 weeks of treatment, the response rates were 30.14%, 25.83% and 25.58% according to the three aforementioned criteria, respectively. Early improvement was a strong predictor of treatment response, with week 3 demonstrating the highest predictive accuracy. CONCLUSION Early improvement with electroacupuncture, especially at week 3, can predict subsequent outcomes. Our findings suggest that acupuncturists may identify non-responders who might require adjustments to therapeutic strategies early in treatment. Please cite this article as: Xiong ZY, Yan SY, Liu SX, Liu ZS. Early improvement to electroacupuncture at week 3 predicts ultimate response in patients with chronic severe functional constipation. J Integr Med. 2025; 23(3): 274-281.
Humans ; Constipation/physiopathology* ; Electroacupuncture ; Male ; Female ; Middle Aged ; Chronic Disease ; Adult ; Treatment Outcome ; Aged

OBJECTIVE: This study aimed to investigate the prevalence of HIV pretreatment drug resistance (PDR) and the transmission clusters associated with PDR-related mutations in newly diagnosed, treatment-naive patients between 2020 and 2023 in Dehong prefecture, Yunnan province, China. METHODS: Demographic information and plasma samples were collected from study participants. PDR was assessed using the Stanford HIV Drug Resistance Database. The Tamura-Nei 93 model within HIV-TRACE was employed to compute pairwise matches with a genetic distance of 0.015 substitutions per site. RESULTS: Among 948 treatment-naive individuals with eligible sequences, 36 HIV subtypes were identified, with unique recombinant forms (URFs) being the most prevalent (18.8%, 178/948). The overall prevalence of PDR was 12.4% (118/948), and resistance to non-nucleotide reverse transcriptase inhibitors (NNRTIs), nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) was 10.7%, 1.3%, and 1.6%, respectively. A total of 91 clusters were identified, among which eight showed evidence of PDR strain transmission. The largest PDR-associated cluster consisted of six CRF01_AE drug-resistant strains carrying K103N and V179T mutations; five of these individuals had initial CD4+ cell counts < 200 cells/μL. CONCLUSION The distribution of HIV subtypes in Dehong is diverse and complex. PDR was moderately prevalent (12.4%) between 2020 and 2023. Evidence of transmission of CRF01_AE strains carrying K103N and V179T mutations was found. Routine surveillance of PDR and the strengthening of control measures are essential to limit the spread of drug-resistance HIV strains.
Humans ; HIV Infections/virology* ; China/epidemiology* ; Drug Resistance, Viral ; Male ; Adult ; Female ; Middle Aged ; HIV-1/genetics* ; Anti-HIV Agents/therapeutic use* ; Myanmar/epidemiology* ; Young Adult ; Prevalence ; Adolescent ; Mutation

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.