Unveiling the cytotoxicity of a new gold(I) complex towards hepatocellular carcinoma by inhibiting TrxR activity.

Hepatocellular carcinoma (HCC), the predominant type of liver cancer, is an aggressive malignancy with limited therapeutic options. In this study, we assess a collection of newly designed gold(I) phosphine complexes. Remarkably, the compound GC002 exhibits the greatest toxicity to HCC cells and outperforms established medications, such as sorafenib and auranofin, in terms of antitumor efficacy. GC002 triggers irreversible necroptosis in HCC cells by increasing the intracellular accumulation of reactive oxygen species (ROS). Mechanistically, GC002 significantly suppresses the activity of thioredoxin reductase (TrxR), which plays a crucial role in regulating redox homeostasis and is often overexpressed in HCC by binding directly to the enzyme. Our in vivo xenograft study confirms that GC002 possesses remarkable antitumor activity against HCC without severe side effects. These findings not only highlight the novel mechanism of controlling necroptosis via TrxR and ROS but also identify GC002 as a promising candidate for the further development of antitumor agents targeting HCC.

Using 3D facial information to predict malnutrition and consequent complications.

BACKGROUND: Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL). METHODS: From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset. RESULTS: Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74. CONCLUSION: Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.

Economic Impact of Postoperative Urinary Retention in the US Hospital Setting.

Background: Postoperative urinary retention (POUR) is a common and distressing surgical complication that may be associated with the pharmacological reversal technique of neuromuscular blockade (NMB). Objective: This study aimed to investigate the impact that POUR has on medical charges. Methods: This was a retrospective observational study of adult patients undergoing select surgeries who were administered neuromuscular blockade agent (NMBA), which was pharmacologically reversed between February 2017 and November 2021 using data from the PINC-AI™ Healthcare Database. Patients were divided into 2 groups: those experiencing POUR (composite of retention of urine, insertion of temporary indwelling bladder catheter, insertion of non-indwelling bladder catheter) during index hospitalization following surgery and those without POUR. Surgeries in inpatient and outpatient settings were analyzed separately. A cross-sectional comparison was performed to report total hospital charges for the 2 groups. Furthermore, patients experiencing subsequent POUR events within three days after discharge from index hospitalization were studied. Results: A total of 330 838 inpatients and 437 063 outpatients were included. POUR developed in 13 020 inpatients and 2756 outpatients. Unadjusted results showed that POUR was associated with greater charges in both inpatient ( 92   529 w i t h P O U R v s 78 556 without POUR, p < .001) and outpatient ( 48   996 w i t h P O U R v s 35 433 without POUR, p < .001) settings. After adjusting for confounders, POUR was found to be associated with greater charges with an overall mean adjusted difference of 10   668 ( 95 95 760- 11   760 , p < .001 ) i n i n p a t i e n t a n d 13 160 (95% CI 11   750 - 14  571, p < .001) in outpatient settings. Charges associated with subsequent POUR events following discharge ranged from 9418 i n p a t i e n t c h a r g e s t o 1694 outpatient charges. Conclusions: Surgical patients who were pharmacologically reversed for NMB and developed a POUR event incurred greater charges than patients without POUR. These findings support the use of NMB reversal agents associated with a lower incidence of POUR.

Plant species diversification in the Himalaya-Hengduan Mountains region: an example from an endemic lineage of Pedicularis (Orobanchaceae) in the role of floral specializations and rapid range expansions.

The c. 270 endemic species of Pedicularis in the Himalaya-Hengduan Mountains (HHM) region exhibit high diversity in geographic distribution, elevational range and floral morphology. Many of these, including the species with the longest corolla tubes and beaked galeas, are monophyletic and represent a putative in situ radiation. In this study, we focus on the representative Clade 3 within the HHM region. We integrate the plastid phylogeny of this clade with environmental data and species distributions to infer environmental correlates of species diversity. We estimate macroevolutionary rates and reconstructed ancestral states for geographic ranges and corolla traits, and analyse patterns of range overlap and niche evolution to assess drivers of diversification in the HHM region. Our results show that the region from northwest Yunnan to southwest Sichuan is the centre of diversity for this clade of Pedicularis. Rates of diversification are associated with precipitation and multiple environmental factors. Multiple range expansions from the Sanjiang (Three Parallel Rivers) region, followed by allopatric speciation across the HHM region, contributed to early rapid diversification. Corolla traits are not significantly associated with species diversification. This study highlights the importance of integrated evidence for understanding species diversification dynamics and contributes to our understanding of the origins of the remarkable richness of plant species in the HHM region.

Chromosome-level and haplotype-resolved genome assembly of Dracaena cambodiana (Asparagaceae).

Dracaena cambodiana Pierre ex Gagn. (Asparagaceae) is the source plant of Dragon's blood and has high ornamental values in gardening. Currently, this species is classified as the second-class state-protected species in the National Key Protected Wild Plants (NKPWP) of China. However, limited genomic data has hindered a more comprehensive scientific understanding of the processes involved in the production of Dragon's blood and the related conservation genomics research. In this study, we assembled a haplotype-resolved genome of D. cambodiana. The haploid genomes, haplotype A and haplotype B, are 1,015.22 Mb and 1,003.13 Mb in size, respectively. The completeness of haplotype A and haplotype B genomes was 98.60% and 98.20%, respectively, using the "embryophyta_10" dataset. Haplotype A and haplotype B genomes contained 27,361 and 27,066 protein-coding genes, respectively, with nearly all being functionally annotated. These findings provide new insights into the genomic characteristics of D. cambodiana and will offer additional genomic resources for studying the biosynthesis mechanism of Dragon's blood and the horticultural application of Dragon trees.

TriCI: Triple Cross-Intra Branch Contrastive Learning for Point Cloud Analysis.

Whereas contrastive learning eliminates the need for labeled data, existing methods may suffer from inadequate features due to the conventional single shared encoder structure and struggle to fully harness the rich spectrum of 3D augmentations. In this paper, we propose TriCI, a self-supervised method that designs a triple-branch contrastive learning architecture. During contrastive pre-training, we generate three augmented versions of each input point cloud sample and pair each augmented sample with the original one, resulting in three unique positive pairs. We subsequently feed the pairs into three distinct encoders, each of which extracts features from its corresponding input positive pair. We design a novel cross-branch contrastive loss and use it along with the intra-branch contrastive loss to jointly train our network. The proposed cross-branch loss effectively aligns the output features from different perspectives for pre-training and facilitates their integration for downstream tasks, particularly in object-level scenarios. The intra-branch loss helps maximize the feature correspondences within positive pairs. Extensive experiments demonstrate the superiority of our TriCI in self-supervised learning, and show its strong ability in enhancing the performance of downstream object classification and part segmentation tasks. Interestingly, our TriCI achieves a 92.9% accuracy for linear SVM evaluation on ModelNet40, exceeding its closest competitor by 1.7% and even exceeding some supervised methods.

DGA-grafting pyridine for ultra-selective and prior extraction of 99TcO4- from simulated spent nuclear fuel.

Selective and prior extraction of 99TcO4- ahead of uranium and plutonium separation is a beneficial strategy for the modern nuclear fuel cycle. Herein, a novel DGA-grafting pyridine ligand BisDODGA-DAPy (L1) was tailored for the efficient separation of TcO4- from simulated spent nuclear fuel based on the selectivity of pyridine and synergistic effect of diglycolamide (DGA) group. Compared to the ligands BisDOSCA-DAPy (L2) and BisDODGA-MPDA (L3) with similar structure, BisDODGA-DAPy (L1) demonstrated the better separation performance including good extraction efficiency, reusability, and high loading capacity for TcO4- under high acidic medium. The interactions of the ligands with Tc(VII)/Re(VII) have been investigated in detail using FT-IR, 1H NMR titration, UV-Vis spectrophotometric titration, ESI-HRMS and DFT simulations. The extraction mechanism affected by the protonation of ligand was elucidated under different acidity. BisDODGA-DAPy (L1) demonstrated the ultra-selective extraction ability for TcO4- from simulated spent nuclear fuel. The maximum SFTc/U and SFTc/Pu values were up to 1.29 × 104 and 5.08 × 103, respectively. In the presence of 9 × 104-fold excess of NO3-, the extraction of TcO4- was almost unaffected. Moreover, the good radiolytic stability further highlights the promising potential of this ligand for 99Tc separation. DFT calculation revealed the dominant role of DAPy and DODGA in TcO4- extraction, providing the theoretical evidence for BisDODGA-DAPy (L1) to selectively bind TcO4- over NO3-.

Cu1.94S-ZnS-CdS ternary heteronanoplates with efficient carrier transfer for enhanced photocatalytic hydrogen evolution.

Incorporating precise morphology control and efficient carrier separation into single-nanoparticle heterojunctions to achieve high photocatalytic efficiency remains a significant challenge. Here, we synthesized Cu1.94S-ZnS-CdS ternary heteronanoplates (HNPs) with a continuous sublattice structure using cation exchange reactions. Femtosecond transient absorption spectroscopy (TAS) confirms that ternary heterojunction enhances carrier separation efficiency, demonstrating both rapid separation (∼0.2 ps) and an extended lifetime (∼1512 ps). The synergistic combination results in a significantly enhanced hydrogen evolution rate of 2.012 mmol·g-1·h-1, which is 17 times and 183 times higher than that achieved by pure CdS and ZnS, respectively. Furthermore, there is no significant decrease in the activity of Cu1.94S-ZnS-CdS in photocatalytic hydrogen evolution after 288 days of placement. Our work offers an alternative approach for designing noble-metal-free photocatalysts with precisely defined materials and interfaces, aiming to enhance both photocatalytic hydrogen evolution efficiency and stability.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Performance Improvement and Application of Degradable Poly-l-lactide and Yttrium-Doped Zinc Oxide Hybrid Films for Energy Harvesting.

Piezoelectric nanogenerators (PENGs) are booming for energy collection and wearable energy supply as one of the next generations of green energy-harvesting devices. Balancing the output, safety, degradation, and cost is the key to solving the bottleneck of PENG application. In this regard, yttrium (Y)-doped zinc oxide (ZnO) (Y-ZnO) was synthesized and embedded into polylactide (PLLA) for developing degradable piezoelectric composite films with an enhanced energy-harvesting performance. The synthesized Y-ZnO exhibits high piezoelectric properties benefiting from the stronger polarity of the Y-O bond and regulation of oxygen vacancy concentration, which improve the output performance of the composite film with Y-ZnO and PLLA (Y-Z-PLLA). The obtained open-circuit voltage (Voc), short-circuit current (Isc), and instantaneous power density of the optimized Y-Z-PLLA PENG reach 17.52 V, 2.45 µA, and 1.76 µW/cm2, respectively. The proposed PENG also shows good degradability. In addition, practical applications of the proposed PENG were demonstrated by converting biomechanical energy, such as walking, running, and jumping, into electricity.

A case report of right ventricular defibrillator and left bundle branch area leads placement and atrioventricular node ablation with chronic right ventricular thrombus.

Despite lack of concrete evidence, right ventricular thrombus is generally considered to be a contraindication for intracardiac lead placement. We present a case of successful placement of a right ventricular defibrillator lead and left bundle branch pacing lead and atrioventricular node ablation in a patient with chronic right ventricle thrombus.

A new gold(I) phosphine complex induces apoptosis in prostate cancer cells by increasing reactive oxygen species.

Thioredoxin reductase (TrxR) is a pivotal regulator of redox homeostasis. It is frequently overexpressed in various cancer cells, including prostate cancer, making it a promising target for the development of anti-cancer drugs. In this study, we screened a series of newly designed complexes of gold(I) phosphine. Specifically, Compound 5 exhibited the highest cytotoxicity against prostate cancer cells and demonstrated stronger antitumor effects than commonly used drugs, such as cisplatin and auranofin. Importantly, our mechanistic study revealed that Compound 5 effectively inhibits the TrxR system in vitro. Additionally, Compound 5 promoted intracellular accumulation of reactive oxygen species (ROS), leading to mitochondrial dysfunction and irreversible apoptosis in prostate cancer cells. Our in vivo xenograft study further demonstrated that Compound 5 has excellent antitumor activity against prostate cancer cells, but does not cause severe side effects. These findings provide a promising lead Compound for the development of novel antitumor agents targeting prostate cancer and offer a valuable tool for investigating biological pathways involving TrxR and ROS modulation.

BCMA-CD19 compound CAR T cells for systemic lupus erythematosus: a phase 1 open-label clinical trial.

OBJECTIVES: This study aims to evaluate the safety and efficacy of BCMA-CD19 compound chimeric antigen receptor T cells (cCAR) to dual reset the humoral and B cell immune system in patients with systemic lupus erythematosus (SLE) with lupus nephritis (LN). METHODS: This is a single-arm open-label multicentre phase 1 study of BCMA and CD19-directed cCAR in patients suffering from SLE/LN with autoantibodies produced by B cells and plasma/long-lived plasma cells. In this clinical trial, we sequentially assigned biopsy-confirmed (classes III-V) LN patients to receive 3×106 cCAR cells/kg postcessation of all SLE medications and conditioning. The primary endpoint of safety and toxicity was assessed. Complete immune reset was indicated by B cell receptor (BCR) deep sequencing and flow cytometry analysis. Patient 11 (P11) had insufficient lymphocyte counts and was underdosed as compassionate use. RESULTS: P1 and P2 achieved symptom and medication-free remission (MFR) from SLE and complete remission from lymphoma. P3-P13 (excluding P11) received an initial dose of 3×106 cCAR cells /kg and were negative for all autoantibodies, including those derived from long-lived plasma cells, 3 months post-cCAR and the complement returned to normal levels. These patients achieved symptom and MFR with post-cCAR follow-up to 46 months. Complete recovery of B cells was seen in 2-6 months post-cCAR. Mean SLE Disease Activity Index 2000 reduced from 10.6 (baseline) to 2.7 (3 months), and renal function significantly improved in 10 LN patients ≤90 days post-cCAR. cCAR T therapy was well tolerant with mild cytokine-release syndrome. CONCLUSIONS: Data suggest that cCAR therapy was safe and effective in inducing MFR and depleting disease-causing autoantibodies in patients with SLE.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.

BACKGROUND: The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud. METHODS: A group of 482 patients were studied in this perspective work. The 3D facial data were obtained using a 3D camera and represented as a 3D facial points cloud. A DL model, PointNet++, was trained and evaluated using the points cloud as inputs and classified the malnutrition states. The performance was evaluated with the area under the receiver operating characteristic curve, accuracy, specificity, sensitivity, and F1 score. RESULTS: Among the 482 patients, 150 patients (31.1%) were diagnosed as having moderate malnutrition and 54 patients (11.2%) as having severe malnutrition. The DL model achieved the performance with an area under the receiver operating characteristic curve of 0.7240 ± 0.0416. CONCLUSION: The DL model achieved encouraging performance in accurately classifying nutrition states based on a points cloud of 3D facial information of patients with malnutrition.

Capsaicin attenuates Porphyromonas gingivalis-suppressed osteogenesis of periodontal ligament stem cells via regulating mitochondrial function and activating PI3K/AKT/mTOR pathway.

BACKGROUND AND OBJECTIVE: Prevention of periodontal bone resorption triggered by Porphyromonas gingivalis (P. gingivalis) is crucial for dental stability. Capsaicin, known as the pungent ingredient of chili peppers, can activate key signaling molecules involved in osteogenic process. However, the effect of capsaicin on osteogenesis of periodontal ligament stem cells (PDLSCs) under inflammation remains elusive. METHODS: P. gingivalis culture suspension was added to mimic the inflammatory status after capsaicin pretreatment. The effects of capsaicin on the osteogenesis of PDLSCs, as well as mitochondrial morphology, Ca2+ level, reactive oxygen species (ROS), mitochondrial membrane potential (MMP), and osteogenesis-regulated protein expression levels were analyzed. Furthermore, a mouse experimental periodontitis model was established to evaluate the effect of capsaicin on alveolar bone resorption and the expression of osteogenesis-related proteins. RESULTS: Under P. gingivalis stimulation, capsaicin increased osteogenesis of PDLSCs. Not surprisingly, capsaicin rescued the damage to mitochondrial morphology, decreased the concentration of intracellular Ca2+ and ROS, enhanced MMP and activated phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway. The in vivo results showed that capsaicin significantly attenuated alveolar bone loss and augmented the expression of bone associated proteins. CONCLUSION: Capsaicin increases osteogenesis of PDLSCs under inflammation and reduces alveolar bone resorption in mouse experimental periodontitis.

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

OBJECTIVES: To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses. METHODS: Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians. RESULTS: The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P <0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P <0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 ( P =0.864). CONCLUSIONS: AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.

A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning.

BACKGROUND: Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. OBJECTIVES: Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. ANIMALS: 200 cats and 200 dogs treated between March 2022 and May 2022. METHODS: A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. RESULTS: The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. CONCLUSION: The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.

Effects of Prepartum L-Tryptophan Supplementation on the Postpartum Performance of Holstein Cows.

The negative energy balance occurring in the periparturient period of cows will impede their health and postpartum performance. To target this issue, L-tryptophan was supplied to the prepartum cows. The results showed that L-tryptophan supplementation significantly increased the serum melatonin level and was accompanied with increases in SOD activity, IL-10 and colostrum IgA levels as well as decreases in MDA and IL-6 levels compared to the control cows. The incidence of postpartum diseases was significantly lower and the pregnancy rate was significantly higher in cows fed L-tryptophan than in the control group. A striking observation was that prepartum L-tryptophan supplementation not only improved the milk production but also the quality compared to the control cows. In general, supplementation with L-tryptophan in the prepartum period can improve the postpartum reproduction and lactation performance of cows to some extent.