Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

OBJECTIVE: The aim of this study is to investigate the independent risk factors of benign prostatic hyperplasia (BPH) complicated with bladder stones, and construct a nomogram prediction model for clinical progression of bladder stones in patients with BPH. METHODS: The clinical data of 368 BPH patients who underwent transurethral resection of the prostate in the Second Affiliated Hospital of Zhengzhou University from January 2018 to January 2021 were retrospectively analyzed. Patients with BPH were divided into group 1 (with bladder stones, n=94) and group 2 (without bladder stones, n=274). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of bladder stones in patients with BPH. A nomogram model was developed, and the areas under the ROC curve and calibration curve were calculated to assess the accuracy of clinical application. RESULTS: Logistic analysis showed that age (HR:1.075,95%CI:1.032 to 1.120), hypertension (HR:2.801,95%CI:1.520 to 5.161), blood uric acid (HR:1.006,95%CI:1.002 to 1.010), intravesical prostatic protrusion (HR:1.189,95%CI1.119 to 1.264）, prostatic urethral angel（HR:1.127,95%CI:1.078to 1.178）were independent risk factors for bladder stones in patients with BPH. The discrimination of the nomogram model based on independent risk factors to predict the occurrence of bladder stones in patients with BPH was 0.874. CONCLUSION The nomogram model can predict the risk of bladder stones in BPH patients with good differentiation and calibration, which is a good guide for clinical work on BPH patients with high risk of bladder stones.
Humans ; Male ; Prostatic Hyperplasia/complications* ; Nomograms ; Urinary Bladder Calculi/etiology* ; Retrospective Studies ; Risk Factors ; Aged ; Logistic Models ; Middle Aged ; ROC Curve ; Transurethral Resection of Prostate

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

Purpose::To identify the risk factors for training-related lower extremity muscle injuries in young males by a non-invasive method of body composition analysis.Methods::A total of 282 healthy young male volunteers aged 18 -20 years participated in this cohort study. Injury location, degree, and injury rate were adjusted by a questionnaire based on the overuse injury assessment methods used in epidemiological studies of sports injuries. The occurrence of training injuries is monitored and diagnosed by physicians and treated accordingly. The body composition was measured using the BodyStat QuadScan 4000 multifrequency Bio-impedance system at 5, 50, 100 and 200 kHz to obtain 4 impedance values. The Shapiro-Wilk test was used to check whether the data conformed to a normal distribution. Data of normal distribution were shown as mean ± SD and analyzed by t-test, while those of non-normal distribution were shown as median (Q 1, Q 3) and analyzed by Wilcoxon rank sum test. The receiver operator characteristic curve and logistic regression analysis were performed to investigate risk factors for developing training-related lower extremity injuries and accuracy. Results::Among the 282 subjects, 78 (27.7%) developed training injuries. Lower extremity training injuries revealed the highest incidence, accounting for 23.4% (66 cases). These patients showed higher percentages of lean body mass ( p = 0.001), total body water (TBW, p=0.006), extracellular water ( p=0.020) and intracellular water ( p=0.010) as well as a larger ratio of basal metabolic rate/total weight ( p=0.006), compared with those without lower extremity muscle injuries. On the contrary, the percentage of body fat ( p=0.001) and body fat mass index ( p=0.002) were lower. Logistic regression analysis showed that TBW percentage > 65.35% ( p=0.050, odds ratio =3.114) and 3rd space water > 0.95% ( p=0.045, odds ratio =2.342) were independent risk factors for lower extremity muscle injuries. Conclusion::TBW percentage and 3rd space water measured with bio-impedance method are potential risk factors for predicting the incidence of lower extremity muscle injuries in young males following training.

Cuproptosis is a new type of cell death that depends on intracellular copper accumulation to trigger the aggregation of mitochondrial lipoacylated protein and the degradation of iron-sulfur cluster protein,with a different mechanism of action from autophagy,ferroptosis,pyroptosis,and necroptosis.Cuproptosis is closely association with the development of liver cancer and resistance to antitumor drugs,as well as the progression of various liver diseases such as hereditary liver diseases,nonalcoholic fatty liver disease,viral hepatitis,and liver cirrhosis.This article summarizes the mechanism of cuproptosis and its role in liver diseases,in order to provide a reference for further research and treatment of liver diseases.

Objective:To explore the efficacy and safety of domestic bortezomib in combination with lenalidomide and dexamethasone in the treatment of newly diagnosed multiple myeloma (NDMM) .Methods:This multicenter, prospective, single-arm clinical study included 126 patients with NDMM admitted to seven hospitals between December 2019 and January 2022. All patients received domestic bortezomib in combination with lenalidomide and dexamethasone (BLD regimen), and the efficacy, prognostic factors, and safety were analyzed.Results:Among the 126 patients with NDMM, 118 completed four cycles of treatment, with an overall response rate (ORR) of 93.22% (110/118) and a ≥very good partial response (VGPR) rate of 68.64% (81/118). Ultimately, 114 patients completed at least eight cycles of treatment, with an ORR of 92.98% (106/114) and a ≥VGPR rate of 77.19% (88/114). Eighteen patients underwent autologous hematopoietic stem cell transplantation after completing 6-8 cycles of the BLD regimen, with an ORR of 100% (18/18) and a ≥VGPR rate of 88.9% (16/18). The proportion of patients achieving ≥VGPR increased with the treatment duration, and factors such as staging and age did not significantly affect efficacy. Single-factor analysis showed that R2-ISS stage Ⅲ/Ⅳ, blood calcium >2.27 mmol/L, and failure to achieve VGPR after six cycles were adverse prognostic factors for progression-free survival (PFS) ( P<0.05), whereas failure to achieve VGPR after six cycles was an adverse prognostic factor for overall survival (OS) ( P<0.001). Multifactor analysis demonstrated that failure to achieve VGPR after six cycles is an independent adverse prognostic factor for PFS ( P=0.002). The incidence of hematologic adverse reactions was 16.7% (19/114), and nonhematologic adverse reactions were mainly mild to moderate, with no significant cardiac or renal adverse reactions observed. Conclusion:The BLD regimen is effective in treating NDMM, in which patients with high-risk genetic features are still achieving a high ≥VGPR rate, and the overall safety is good.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Glutamate,norepinephrine,and their receptors com-prise the glutamatergic and norepinephrine systems,which mu-tually affect each other and play essential roles in mediating vari-ous neuropsychiatric diseases.This paper reviews the functions of N-methyl-D-aspartate receptor(NMDA-R)and α2-adrenergic receptor(α2-AR)and their functional crosstalk at the molecular level in brain in common neuropsychiatric diseases,which would benefit our understanding of neuropathophysiology of psychiatric diseases,drug development and optimization of clinical neuro-psychopharmacology.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*