Objective: To explore the network structure of middle school students basic psychological needs and psychological behavioral problems, and identify the core nodes within the network, as well as examine demographic subgroup differences, so as to provide support for targeted mental health interventions for adolescents. Methods: In September and October of 2023, a total of 2 000 junior and senior high school students were selected with multistage cluster random sampling from 8 schools in Jiaojiang District and Tiantai County, Taizhou City. An online self administered questionnaire was used to assess emotional and behavioral problems, perceived autonomy, self awareness, loneliness, and social support. The instruments included the Strengths and Difficulties Questionnaire (SDQ), Perceived Choice and Awareness of Self Scale (PCASS), Mental Health Literacy Questionnaire (MHLQ), University of California,Los Angeles Loneliness Scale (UCLA-LS), and the Multidimensional Scale of Perceived Social Support (MSPSS). A network analysis approach was employed to construct a network representing adolescents basic psychological needs and psychological behavioral problems, focusing on centrality measures and demographic subgroup differences. Results: A total of 418 students (20.9%) reported abnormal emotional and behavioral problems. Perceived autonomy and competence were negatively correlated with emotional problems (weights: 0.12, 0.14) and hyperactivity (weights: 0.10, 0.16). Social support showed negative correlation with peer relationship issues, hyperactivity, and conduct problems (weights: 0.16, 0.13, 0.10). Loneliness was positively correlated with emotional symptoms and peer relationship problems (weights: 0.28, 0.18). In the overall network, perceived relationships (social support and loneliness), emotional symptoms, and hyperactivity emerged as central nodes. Significant differences in network structure were observed between gender subgroups ( P =0.02). Girls internalizing issues were more influenced by loneliness and perceived autonomy frustration, while social support exhibited higher centrality in boys. Conclusions Perceived relationships, emotional problems, and hyperactivity are key nodes in the network of adolescents basic psychological needs and psychological behavioral problems. Loneliness demonstrates a prominent influence within the network, and the overall network exhibits gender differences.

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

The translocator protein (TSPO) positron emission tomography (PET) can noninvasively detect neuroinflammation associated with epileptogenesis and epilepsy. This study explored the role of the TSPO-targeting radioligand [¹⁸F]F-TFQC, an m-trifluoromethyl ER176 analog, in the PET neuroimaging of epileptic rats. Initially, [¹⁸F]F-TFQC was synthesized with a radiochemical yield of 8%-10% (EOS), a radiochemical purity of over 99%, and a specific activity of 38.21 ± 1.73 MBq/nmol (EOS). After determining that [¹⁸F]F-TFQC exhibited good biochemical properties, [¹⁸F]F-TFQC PET neuroimaging was performed in epileptic rats at multiple time points in various stages of disease progression. PET imaging showed specific [¹⁸F]F-TFQC uptake in the right hippocampus (KA-injected site, i.e., epileptogenic zone), which was most pronounced at 1 week (T/NT 1.63 ± 0.21) and 1 month (T/NT 1.66 ± 0.20). The PET results were further validated using autoradiography and pathological analysis. Thus, [¹⁸F]F-TFQC can reflect the TSPO levels and localize the epileptogenic zone, thereby offering the potential for monitoring neuroinflammation and guiding anti-inflammatory treatment in patients with epilepsy.

Hypertrophic scar is a fibrous hyperplastic disorder that arises from skin injuries. The current therapeutic modalities are constrained by the dense and rigid scar tissue which impedes effective drug delivery. Additionally, insufficient autophagic activity in fibroblasts hinders their apoptosis, leading to excessive matrix deposition. Here, we developed an active microneedle (MN) system to overcome these challenges by integrating micromotor-driven drug delivery with autophagy regulation to remodel the scar microenvironment. Specifically, sodium bicarbonate and citric acid were introduced into the MNs as a built-in engine to generate CO₂ bubbles, thereby enabling enhanced lateral and vertical drug diffusion into dense scar tissue. The system concurrently encapsulated curcumin (Cur), an autophagy activator, and triamcinolone acetonide (TA), synergistically inducing fibroblast apoptosis by upregulating autophagic activity. In vitro studies demonstrated that active MNs achieved efficient drug penetration within isolated scar tissue. The rabbit hypertrophic scar model revealed that TA-Cur MNs significantly reduced the scar elevation index, suppressed collagen I and transforming growth factor-β1 (TGF-β1) expression, and elevated LC3 protein levels. These findings highlight the potential of the active MN system as an efficacious platform for autonomous augmented drug delivery and autophagy-targeted therapy in fibrotic disorder treatments.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Objective:To evaluate the long-term clinical efficacy and the influence on anal function of surgery combined with ustekinumab (UST) in active Crohn's disease (CD) patients with perianal fistula.Methods:A retrospective cohort study was conducted. Clinical data of active CD patients with perianal fistula undergoing surgery combined with UST at Longhua Hospital of Shanghai University of Traditional Chinese Medicine from August 2020 to December 2022 were collected. The primary endpoints were clinical healing rate, Wexner score, and anorectal manometry values at week 52 of treatment. Secondary endpoints included the Crohn's disease activity index (CDAI), perianal Crohn's disease activity index (PDAI), laboratory indicators [C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin (FC) ], endoscopic remission rate, Van Assche score, and radiographic healing rate at week 52 of treatment.Results:A total of 28 patients were included, with 22 males (78.6%) and 6 females (21.4%) ; age (25.2 ± 7.7) (16.0-52.0) years. There was 1 patient (3.6%) of simple anal fistula. There were 27 patients (96.4%) of complex anal fistulas, including 12 of high intersphincteric type, 15 of high transsphincteric type, in which 15 with branched tracts (or ≥2 fistula tracts). The pre-treatment CDAI was 187.0 (156.0, 245.0), and the PDAI was 10.0 (9.0, 12.0). Among the 28 patients, 23 (82.1%) underwent fistulotomy, 1 (3.6%) underwent transanal opening of intersphincteric space (TROPIS), and 4 (14.3%) underwent video-assisted anal fistula treatment (VAAFT) combined with fistula-tract laser closure (FiLaC). All the patients received UST treatment postoperatively, without concurrent use of immunosuppressants or corticosteroid therapy. At week 52 of treatment, 28 (100%) patients achieved clinical healing. Compared to pre-treatment, Wexner score of patients at week 52 of treatment was significantly lower [0 (0, 0) vs. 1.0 (0, 3.0), P < 0.001], maximum anal sphincter pressure increased [ (137.6±40.9) mmHg vs. (105.1±29.2) mmHg, P < 0.001], maximum anal sphincter contraction time extended [9.0 (5.0, 15.0) s vs. 4.0 (2.0, 6.0) s, P < 0.001], and there was no significant decrease in anal resting pressure ( P > 0.05). Compared to pre-treatment, CDAI, PDAI, Van Assche scores, and simple endoscopic score for Crohn's disease (SES-CD) of patients at week 52 of treatment all significantly decreased (all P < 0.001), and CRP, ESR, and FC all decreased (all P < 0.05), with statistically significant differences. The radiographic healing rate at week 52 of treatment was 75.0% (21/28), and the radiographic remission rate was 92.9% (26/28). The endoscopic remission rate was 57.1% (16/28), and the endoscopic response rate was 82.1% (23/28) . Conclusion:The long-term clinical healing rate of active CD patients with perianal fistula receiving surgery combined with UST is high, and the anal function can be improved significantly.

Objective:To investigate the feasibility and clinical effects of ultra early enteral nutrition (≤24 h) in critically ill children supported by extracorporeal membrane oxygenation (ECMO).Methods:A retrospective cohort study was conducted. Clinical data of 43 critically ill children who received ECMO support in the pediatric intensive care unit (PICU) of Shanghai Children′s Hospital from January 2016 to December 2023 were collected, including general information, nutritional support modalities, and enteral nutrition tolerance. Based on the timing of enteral nutrition initiation, patients were divided into the within 24 h enteral nutrition group and the after 24 h enteral nutrition group. Nutritive indicators, nutritional intake, duration of ECMO support, duration of mechanical ventilation duration, and mortality rates were compared between the 2 groups using the two independent sample t test, Mann-Whitney U test, χ2 test and Fisher′s exact test. Results:Among the 43 children, 25 were male and 18 were female, with an age of 47 (18, 97) months. There were no statistically significant differences between the within 24 h enteral nutrition group (21 cases) and the after 24 h enteral nutrition group (22 cases) in terms of age, body mass index Z score, total protein, albumin, hemoglobin levels before ECMO support, duration of ECMO support, duration of mechanical ventilation, length of PICU stay, number of enteral nutrition intolerance events, number of enteral nutrition interruption, or mortality rate (all P>0.05). The protein intake adequacy rate during ECMO support was higher in the within 24 h enteral nutrition group than in the after 24 h enteral nutrition group (0 (0, 21%) vs. 0 (0, 0), U=175.00, P<0.05). Conclusions:Ultra early enteral nutrition is safe for children supported by ECMO. Initiating enteral nutrition within 24 h can increase the proportion of days with adequate protein intake in ECMO children without increasing the occurance of enteral nutrition intolerance or interruptions.

Objective:To explore the clinical characteristics and healthcare burden in patients with McCune-Albright syndrome (MAS).Methods:A cross-sectional study was conducted at the Children′s Hospital, Zhejiang University School of Medicine. Clinical and healthcare burden data were systematically collected through structured questionnaires in 164 children with MAS from February 2022 to May 2023. According to the clinical characteristics, patients were categorized into 3 groups: monosymptomatic, bisymptomatic and trisymptomatic groups. Patients were also divided into 3 groups according to the age of <7, 7-<10 and 10-18 years. Comparative analyses of clinical characteristics and healthcare burden were conducted across age, sex, and symptom categories.Results:The cohort comprised 59 males (36.0%) and 105 females (64.0%) with an age of 4.6 (2.0, 7.4) years. Age stratification revealed 117 cases (71.3%) aged 0-<7 years, 29 cases (17.7%) aged 7-<10 years, and 18 cases (11.0%) aged 10-<18 years. Among monosymptomatic (67 cases, 40.9%), the cohort comprised 32 females (47.8%) and 35 males (52.2%), predominantly presenting with fibrous dysplasia (57 cases, 85.1%). This subgroup showed peak prevalence in the 0-<7 years age range (29 cases (50.9%)). The bisymptomatic cohort (56 cases, 34.1%) consisted of 39 females (69.6%) and 17 males (30.4%), predominantly manifesting fibrous dysplasia with skin hyperpigmentation (25 cases, 44.6%). Peak prevalence occurred in the 0-<7 years subgroup(16 cases (64.0%)). The trisymptomatic cohort (41 cases, 25.0%) consisted of 34 females (82.9%) and 7 males (17.1%), with peak prevalence occurring in the 0-<7 years subgroup (36 cases (87.8%)). The diagnostic journey analysis revealed 94 cases (57.3%) required 1-3 referrals, and 34 cases (20.7%) necessitated >3 referrals from symptom onset to definitive diagnosis. Healthcare expenditure analysis revealed 69 families (42.1%) incurred direct medical costs of 10 000-100 000 CNY, with 11 families (6.7%) exceeding >100 000 CNY. Direct non-medical costs reached of 10 000-100 000 CNY for 62 families (37.8%) and >100 000 CNY for 4 families (2.4%). Productivity loss affected 58 families (35.4%) at 10 000-100 000 CNY and 8 families (4.9%) above 100 000 CNY during the study period.Conclusion:MAS requires increased attention to skeletal manifestations, especially in children aged 0-<7 years. Moreover, the significant financial burden on families necessitates a society-wide support system.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.