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: 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 To analyze the characteristics of methadone-related poisoning cases and provide a reference for forensic identification.Methods A total of 71 cases of methadone-related poisoning re-ported from 1998 to 2023 in China and 26 cases of methadone-related deaths reported from 2013 to 2018 in Italy were retrieved from databases including PubMed,Wanfang and CNKI.The general infor-mation,forensic pathological and toxicological characteristics were analyzed.Results Among the 71 methadone-related poisoning cases in China,there were 54 cases(76.06%)of poisoning without death and 17 cases(23.94%)of death from poisoning.There were 54 male cases(76.06%),and 51 cases(71.83%)aged 19 to 39 years old.There were 35 cases(49.30%)of poisoning caused by methadone alone,and 32 cases(45.07%)were poisoning caused by methadone combined with other substances or drugs including heroin and benzodiazepines.Most of the poisoned showed coma,respiratory depres-sion and miosis.Signs of asphyxia were often found by autopsy.The mass concentration of methadone detected in the blood of 6 deceased ranged from 0.112 to 3.000 mg/L.Among the 26 methadone-related deaths in Italy,22 cases were male(84.62%).There were 6 cases(23.08%)caused by methadone alone,and 20 cases(76.92%)died from methadone combined with other substances or drugs.The mass concentration of methadone in blood ranged from 0.181 to 4.059 mg/L.Conclusion The propor-tions of poisoning cases caused by methadone alone and methadone combined with other substances or drugs are comparable in China.The majority of deceased caused by methadone poisoning shows typi-cal triad of coma,respiratory depression and miosis,which helps forensic experts determine the cause of death related to methadone.Additionally,it is necessary to increase the routine testing of the con-centration of methadone and its combined substances or drugs in deceased,and collect data for the in-terpretation of the results of related cases.

To report 3 cases of acute generalized exanthematous pustulosis (AGEP) caused by hydroxychloroquine. All the 3 patients were females, aged 23, 30, and 28 years respectively. In cases 1 and 3, the rashes appeared 4 days and 12 days respectively after the treatment with hydroxychloroquine for systemic lupus erythematosus; case 2, who was 8 weeks pregnant, developed rashes 10 days after starting hydroxychloroquine treatment for antiphospholipid syndrome. All the 3 patients had high fever, and clinically presented with generalized round or oval-shaped edematous erythema on the face, neck, trunk and limbs, covered with a large number of pinhead-sized pustules, and with multiple erythema multiforme-like lesions on the trunk and both upper limbs, including targetoid lesions. Mutations in the IL36RN gene were identified in all the 3 patients: a homozygous mutation c.115+6T>C in the IL36RN gene was found in case 1, and her parents were heterozygous carriers; case 2 inherited the heterozygous mutation c.115+6T>C in the IL36RN gene from her mother; the heterozygous mutation c.115+6T>C found in case 3 was a de novo mutation. A diagnosis of AGEP was made in all the 3 cases. Cases 1 and 2 received subcutaneous injections of adalimumab in addition to the treatment of their underlying diseases, and skin lesions markedly regressed after 1 week of treatment; case 3 was treated with high-dose glucocorticoids, and lesions subsided after 4 weeks; no significant adverse reactions were observed in cases 1 and 2, however, femoral head necrosis was noted in case 3. During a follow-up period of 42 months, none of the patients experienced recurrence, and case 2 gave birth to a healthy baby boy after 8-month treatment.

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.