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.

AIM:To evaluate the efficacy of intravitreal ranibizumab injection(IVR)combined with subthreshold micropulse(STMP)in the treatment of diabetic macular edema(DME).METHODS: Retrospective study. A total of 98 DME patients(98 eyes)admitted to our hospital from March 2022 to March 2023 were enrolled and divided into two groups based on treatment methods: the control group(49 eyes)received STMP yellow laser therapy alone, while the study group(49 eyes)underwent combined IVR and STMP yellow laser therapy. Comparisons were made between the two groups regarding best corrected visual acuity(BCVA), retinal neovascularization(RNV)leakage area, parafoveal macular thickness(PMT), foveal macular thickness(FMT), central retinal thickness(CRT), and foveal avascular zone(FAZ)area, quality of life was assessed using the Chinese-version low vision quality of life questionnaire(CLVQOL), and complication rates were recorded. Additionally, serum levels of nitric oxide synthase(NOS)and vascular endothelial growth factor(VEGF)were measured before and after treatment in both groups.RESULTS: At 3 mo after treatment, both groups showed improved BCVA compared to baseline, with reduced RNV leakage area, PMT, FMT, CRT, FAZ, and serum levels of VEGF, while serum NOS levels and all CLVQOL domain scores were higher than pre-treatment(all P<0.05). Furthermore, the study group demonstrated superior outcomes in all these parameters compared to the control group(all P<0.05), and no ocular or systemic complications occurred in any patient.CONCLUSION: IVR combined with STMP yellow laser for DME improves visual acuity, reduces RNV leakage area, PMT, FMT, CRT, and FAZ, modulates serum NOS and VEGF levels, enhances quality of life, and demonstrates good safety.

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 investigate the protective effect of quercetin against 5-fluorouracil(5-FU)-induced damage in the human immortalized keratinocytes(HACAT),and to elucidate its possible mechanism.Methods:The HACAT cells were divided into control group(normal cultured cells),5-FU group(treated with 7.5 mg·L-1 5-FU for 24 h),and low,medium,and high doses of quercetin groups(HACAT cells treated with 25,50,and 75 μmol·L-1 quercetin combined with 7.5 mg·L-15-FU for 24 h).Cell counting kit-8(CCK-8)assay was used to detect the survival rates of HACAT cells treated with different doses(0,10,25,50,75 and 100 μmol·L-1)of quercetin in various groups.The fluorescent probe of reactive oxygen species(ROS)was used to detect ROS levels in the HACAT cells in various groups.Annexin Ⅴ-FITC/PI double staining was used to detect the apoptosis of HACAT cells in various groups.Western blotting method was used to detect the expression levels of B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax),Cleaved Caspase-3,cyclooxygenase-2(COX-2),interleukin-1 β(IL-1β),and interleukin-6(IL-6)in the HACAT cells in various groups.Results:The CCK-8 assay results showed that compared with 0 μmol·L-1 quercetin group,the survival rates of HACAT cells in 10,25,50 and 75 μmol·L-1quercetin groups showed no significant differences(P＞0.05),while the survival rates of HACAT cells in 100 μmol·L-1 quercetin group was significantly decreased(P＜0.05).Compared with 5-FU group,the survival rates of the HACAT cells in low,medium and high doses of quercetin group were significantly increased(P＜0.05).Compared with 5-FU group,the ROS levels in low,medium,and high doses of quercetin groups were significantly decreased(P＜0.05).Annexin Ⅴ-FITC/PI double staining assay showed that compared with 5-FU group,the apoptotic rates in low,medium and high doses of quercetin groups were significantly decreased(P＜0.05).The Western blotting results showed that compared with 5-FU group,the expression levels of Bcl-2 protein in medium and high doses of quercetin groups were significantly increased(P＜0.05),the expression levels of Bax and Cleaved Caspase-3 proteins in low,medium and high doses of quercetin groups were significantly decreased(P＜0.05),the expression levels of COX-2 protein in medium and high doses of quercetin groups were significantly decreased(P＜0.05),and the expression levels of IL-1β and IL-6 proteins in medium dose of quercetin group were significantly decreasd(P＜0.05).Conclusion:Quercetin has protective effect on 5-FU-induced damage in the HACAT cells,and its mechanism may be related to the reduction of the expression of ROS and inflammatory factor COX-2 which attenuate the apoptosis.

Objective To construct and validate a predictive model of post-stroke cognitive impairment(PSCI)in ischemic stroke patients.Methods A total of 517 ischemic stroke patients admitted to the Depart-ment of Neurology of the hospital from January to December 2021 were selected by convenience sampling method,and were randomly divided into 359 cases of the modeling group and 158 cases of the validation group according to a 7∶3 ratio.In the modeling group,logistic regression analysis was used to screen risk factors for PSCI in the modeling group,and a column chart model was constructed.The performance of the column chart model was evaluated using receiver operating characteristic(ROC)curve and area under the curve(AUC),goodness of fit test Hosmer Lemeshow(H-L test),calibration curve,and clinical decision curve(DCA)in the validation group.Results The incidence of cognitive impairment after ischemic stroke in the modeling group was 63.8％(229/359).The AUC value in the column chart model was 0.836,and the H-L test result was x2=7.254,P=0.510.The calibration curve was close to the ideal curve at 45°.DCA was higher than the reference line,and homocysteine,age,NIHSS score,and educational level were the main predictive factors.Conclusion Con-structing a predictive model for post-stroke cognitive impairment in patients with ischemic stroke has good predictive value.

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/.