Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Empathy in undergraduate medical students: a multi-center cross-sectional study in China.

BACKGROUND: Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students' academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear. METHODS: This was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model. RESULTS: We received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P < 0.05). Then, the nomogram was established based on six variables. The validation suggested the nomogram model was well calibrated and had good utility in education, as well as area under the curve of model prediction was 0.65. CONCLUSIONS: We identify factors influencing empathy of undergraduate medical students. Moreover, increasing manifest and hidden curriculums on cultivating empathy of medical students may be needed among medical universities or schools in China.

A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students' grade and their perceptions of the learning environment.

BACKGROUND: Medical school learning environment (MSLE) has a holistic impact on students' psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student's grade in the perception of MSLE. METHODS: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student's grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch's ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student's grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. RESULTS: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265-1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326-1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student's low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). CONCLUSION: The student's grade was a significant influencing factor in medical students' perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students' perceptions are urgently needed.

Unravelling structural features of small molecules for photochemical transformation of environmental contaminants.

Small molecules, including natural metabolites, organic matter decomposition products, and engineered oxidation byproducts, are widespread in aquatic environment. However, the limited understanding of the photochemical interactions of these small molecules with water pollutants hampers the development of effective environmental protection strategies. This study explores the structural features governing the photochemical transformation of toxic oxyanions by α- and ß-dicarbonyl compounds. By integrating experimental observations with quantum chemical calculations, a robust correlation network was constructed. The correlation network reveals that the reactivity of small organic molecules with oxyanions could be quantitively predicted by their intrinsic properties, such as electronic transition energy, bond dissociation energy, molecular softness, molecular orbital gap, atomic charge, and molecular surface local ionization energy. This network maps the relationship between the molecular architecture of chemicals and their photochemical behaviors. This perspective offers fresh insights into the photochemical behaviors of small molecules in diverse environmental and chemical contexts and are helpful for developing advanced water treatment strategies toward a sustainable future.

Organic peroxyl radicals from biacetyl accelerated the visible-light degradation of steroid estrogens in aqueous solution.

Indirect photodegradation is an important pathway for the reduction of steroid estrogens in sunlit surface waters. Nevertheless, the kinetics and mechanisms governing the interaction between coexisting carbonyl compounds and estrogens under visible light (Vis) remain unexplored. This study systematically investigates the Vis-induced photodegradation of 17ß-estradiol (E2) in the presence of five specific carbonyl compounds-biacetyl (BD), acetone, glyoxal, pyruvic acid, and benzoquinone. The results demonstrate that, among these compounds, only BD significantly enhanced the photodegradation of E2 under Vis irradiation (λ > 400 nm). The pseudo-first order photodegradation rate constants (k1) of E2 in the Vis/BD system were 0.025 min-1 and 0.076 min-1 in ultrapure water and river water, respectively. The enhancing effect of BD was found to be pH-dependent, increasing the pH from 3.0 to 11.0 resulted in a 76% reduction in the k1 value of E2 in the Vis/BD system. Furthermore, the presence of humic acid, NO3-, or HCO3- led to an increase of more than 35% in the k1 value of E2, while NO2- exerted a pronounced inhibitory effect, resulting in a 92% decrease. Peroxyacetyl and peroxymethyl radicals, derived from BD in a yield ratio of 9, played a crucial role in the degradation of E2. These peroxyl radicals primarily targeted electron-rich hydroxyl sites of E2, initiating hydroxylation and ring-opening reactions that culminated in the formation of acidic byproducts. Notably, toxicity evaluation indicates that these hydroxylated and acidic products exhibited lower toxicity than the parent compound E2. This study highlights the important role of peroxyl radicals in estrogen degradation within aquatic environment, and also helps to design efficient visible light-responsive photo-activators for the treatment of estrogen-contaminated waters.

Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors.

Machine learning (ML) is becoming an efficient tool for predicting the fate of aquatic contaminants owing to the preponderance of big data. However, whether ML can "learn" the differences in reactivity among different free radicals has not yet been tested. In this work, the effectiveness of combining ML algorithms with molecular fingerprints to predict the reactivity of three free radicals was evaluated. First, a dataset containing 211 organic pollutants and their respective rate constants with the carbonate radical (CO3•-) was used to develop predictive models using both linear regression and ML methods. The use of topological atomic alignment information, in the form of the molecular access system (MACCS) and Morgan Fingerprint, and the electronic structure features (energy levels of the lowest unoccupied and highest occupied molecular orbitals, ELUMO and EHOMO, and the energy gap between ELUMO and EHOMO) gave satisfactory predictive performances (ML model with Random Forest algorithm with MACCS: RMSEtest = 0.787; linear regression model with energy levels: RMSEtest = 0.641). Additionally, the model interpretation correctly described that the key reactivity features for CO3•- were relatively close to those for SO4•- rather than those for •OH. These results suggest that combination of ML algorithms with easily accessible molecular fingerprints would be a powerful tool to accurately predict the radical reactions towards organic compounds.

Identification of Pancreatic Metastasis Cells and Cell Spheroids by the Organelle-Targeting Sensor Array.

Pancreatic cancer is a highly malignant and metastatic cancer. Pancreatic cancer can lead to liver metastases, gallbladder metastases, and duodenum metastases. The identification of pancreatic cancer cells is essential for the diagnosis of metastatic cancer and exploration of carcinoma in situ. Organelles play an important role in maintaining the function of cells, the various cells show significant differences in organelle microenvironment. Herein, six probes are synthesized for targeting mitochondria, lysosomes, cell membranes, endoplasmic reticulum, Golgi apparatus, and lipid droplets. The six fluorescent probes form an organelles-targeted sensor array (OT-SA) to image pancreatic metastatic cancer cells and cell spheroids. The homology of metastatic cancer cells brings the challenge for identification of these cells. The residual network (ResNet) model has been proven to automatically extract and select image features, which can figure out a subtle difference among similar samples. Hence, OT-SA is developed to identify pancreatic metastasis cells and cell spheroids in combination with ResNet analysis. The identification accuracy for the pancreatic metastasis cells (> 99%) and pancreatic metastasis cell spheroids (> 99%) in the test set is successfully achieved respectively. The organelles-targeting sensor array provides a method for the identification of pancreatic cancer metastasis in cells and cell spheroids.

Bioactive Patch for Rotator Cuff Repairing via Enhancing Tendon-to-Bone Healing: A Large Animal Study and Short-Term Outcome of a Clinical Trial.

Tissue engineering has demonstrated its efficacy in promoting tissue regeneration, and extensive research has explored its application in rotator cuff (RC) tears. However, there remains a paucity of research translating from bench to clinic. A key challenge in RC repair is the healing of tendon-bone interface (TBI), for which bioactive materials suitable for interface repair are still lacking. The umbilical cord (UC), which serves as a vital repository of bioactive components in nature, is emerging as an important source of tissue engineering materials. A minimally manipulated approach is used to fabricate UC scaffolds that retain a wealth of bioactive components and cytokines. The scaffold demonstrates the ability to modulate the TBI healing microenvironment by facilitating cell proliferation, migration, suppressing inflammation, and inducing chondrogenic differentiation. This foundation sets the stage for in vivo validation and clinical translation. Following implantation of UC scaffolds in the canine model, comprehensive assessments, including MRI and histological analysis confirm their efficacy in inducing TBI reconstruction. Encouraging short-term clinical results further suggest the ability of UC scaffolds to effectively enhance RC repair. This investigation explores the mechanisms underlying the promotion of TBI repair by UC scaffolds, providing key insights for clinical application and translational research.

Fluffy hybrid nanoadjuvants for reversing the imbalance of osteoclastic and osteogenic niches in osteoporosis.

Osteoporosis is majorly caused by an imbalance between osteoclastic and osteogenic niches. Despite the development of nationally recognized first-line anti-osteoporosis drugs, including alendronate (AL), their low bioavailability, poor uptake rate, and dose-related side effects present significant challenges in treatment. This calls for an urgent need for more effective bone-affinity drug delivery systems. In this study, we produced hybrid structures with bioactive components and stable fluffy topological morphology by cross-linking calcium and phosphorus precursors based on mesoporous silica to fabricate nanoadjuvants for AL delivery. The subsequent grafting of -PEG-DAsp8 ensured superior biocompatibility and bone targeting capacity. RNA sequencing revealed that these fluffy nanoadjuvants effectively activated adhesion pathways through CARD11 and CD34 molecular mechanisms, hence promoting cellular uptake and intracellular delivery of AL. Experiments showed that small-dose AL nanoadjuvants effectively suppress osteoclast formation and potentially promote osteogenesis. In vivo results restored the balance between osteogenic and osteoclastic niches against osteoporosis as well as the consequent significant recovery of bone mass. Therefore, this study constructed a drug nanoadjuvant with peculiar topological structures and high bone targeting capacities, efficient intracellular drug delivery as well as bone bioactivity. This provides a novel perspective on drug delivery for osteoporosis and treatment strategies for other bone diseases.

ZrO2 Superhydrophobic Coating with an Excellent Corrosion Resistance and Stable Degradation Performance on Zr-Based Bulk Metallic Glass.

Photocatalysis is an energy-saving and high-efficiency green environmental technology. Because of its wide band gap and low light utilization, few studies have been conducted on ZrO2 used as a photocatalytic material. In this paper, a corrosion-resistant superhydrophobic ZrO2 coating was prepared on the surface of Zr-based bulk metallic glass by electrochemical etching. This coating not only showed a better corrosion resistance and easier collection, but also presented a stable degradation performance when combined with H2O2; these characteristics are necessary for photocatalysts to survive under harsh environments. This study provides a new direction for designing superhydrophobic surfaces on bulk metallic glass that possess a functional performance.