Cut-off value of glycated hemoglobin A1c for detecting diabetic retinopathy in the Chinese population.

BACKGROUND: Glycated hemoglobin A1c (HbA1c) is considered the most suitable for diabetes mellitus diagnosis due to its accuracy and convenience. However, the effect of HbA1c on diabetic retinopathy (DR) in the Han and Korean populations in Jilin, China, remains inconclusive. AIM: To determine the best cut-off of HbA1c for diagnosing DR among the Chinese. METHODS: This cross-sectional study included 1933 participants from the Yanbian area of Jilin Province, China. Trained investigators employed a questionnaire-based survey, physical examination, laboratory tests, and fundus photography for the investigation. The best cut-off value for HbA1c was established via the receiver operating characteristic curve. The factors associated with HbA1c-associated risk factors were determined via linear regression. RESULTS: The analysis included 887 eligible Chinese Han and Korean participants, 591 of whom were assigned randomly to the training set and 296 to the validation set. The prevalence of DR was 3.27% in the total population. HbA1c of 6.2% was the best cut-off value in the training set, while it was 5.9% in the validation set. In both Chinese Han and Korean populations, an HbA1c level of 6.2% was the best cut-off value. The optimal cut-off values of fasting blood glucose (FBG) ≥ 7 mmol/L and < 7 mmol/L were 8.1% and 6.2% respectively in Han populations, while those in Korean populations were 6.9% and 5.3%, respectively. Age, body mass index, and FBG were determined as the risk factors impacting HbA1c levels. CONCLUSION: HbA1c may serve as a useful diagnostic indicator for DR. An HbA1c level of 6.2% may be an appropriate cut-off value for DR detection in the Chinese population.

Ferritin Heavy-like subunit is involved in the innate immune defense of the red swamp crayfish Procambarus clarkii.

Iron-binding proteins, known as ferritins, play pivotal roles in immunological response, detoxification, and iron storage. Despite their significance to organisms, little is known about how they affect the immunological system of the red swamp crayfish (Procambarus clarkii). In our previous research, one ferritin subunit was completely discovered as an H-like subunit (PcFeH) from P. clarkii. The full-length cDNA of PcFerH is 1779 bp, including a 5'-UTR (untranslated region, UTR) of 89 bp, 3'-UTR (untranslated region, UTR) of 1180 bp and an ORF (open reading frame, ORF) of 510 bp encoding a polypeptide of 169 amino acids that contains a signal peptide and a Ferritin domain. The deduced PcFerH protein sequence has highly identity with other crayfish. PcFerH protein's estimated tertiary structure is quite comparable to animal structure. The PcFerH is close to Cherax quadricarinatus, according to phylogenetic analysis. All the organs examined showed widespread expression of PcFerH mRNA, with the ovary exhibiting the highest levels of expression. Additionally, in crayfish muscles, intestines, and gills, the mRNA transcript of PcFerH was noticeably up-regulated, after LPS and Poly I:C challenge. The expression of downstream genes in the immunological signaling system was suppressed when the PcFerH gene was knocked down. All of these findings suggested that PcFerH played a vital role in regulating the expression of downstream effectors in the immunological signaling pathway of crayfish.

ZnIn2Se4 nanoparticles photocatalyst for efficient solar fuel production.

Selecting a suitable photocatalyst to establish the Z-scheme heterojunction which is accompanied by effective photogenerated hole and electron separation, is one of the advantageous strategies for efficient photocatalytic solar energy conversion. Therefore, we prepared a ZnIn2Se4 nanoparticles photocatalyst to build a double Z-scheme heterojunction with mixed-phase TiO2 nanofibers, boosting photocatalytic solar fuel preparation. The result of X-ray photoelectron spectroscopy confirmed the existence of interfacial chemical bonds and internal electric fields. The interfacial Ti-Se bond is regarded as a channel and the internal electric field serves as the driving force for electron transfer. And the composite photocatalyst exhibits a great hydrogen evolution rate of 0.11 mmol g-1 h-1. From a forward-working perspective, this work proposes a ZnIn2Se4 nanoparticles photocatalyst for efficient solar fuel conversion, promoting the application of bimetallic selenide photocatalyst in the field of photocatalysis.

Effects and Action Mode of Oleic Acid and Azone on Release and Penetration Process of Levothyroxine Sodium Patches.

In recent years, there has been a significant increase in the prevalence of thyroid diseases, particularly hypothyroidism. In this study, we investigated the impact and mechanisms of Chemical permeation enhancement(CPE) on transdermal permeation of levothyroxine sodium (L-T4) patches.We found that the combination of oleic acid (OA) and Azone (NZ) yielded the best transdermal permeation effect for L-T4.Subsequently, we also investigated the relevant propermeability mechanism.The results demonstrate that the combined application of OA and NZ significantly enhances the transdermal permeation of L-T4 compared to individual applications,it is attributed to two mechanisms: firstly, OA improves drug release by increasing the flowability of the pressure-sensitive adhesive (PSA) matrix; secondly, both OA and NZ act on the stratum corneum, especially facilitating L-T4 permeation through the hair follicle pathway. No skin irritation or cytotoxicity is observed with these final patches, which exhibit a remarkable therapeutic effect on hypothyroidism. this study contributes to the development of transdermal formulations of L-T4.

Development and validation of the eMCI-CHD tool: A multivariable prediction model for the risk of mild cognitive impairment in patients with coronary heart disease.

OBJECTIVE: This study aimed to develop and validate an eMCI-CHD tool based on clinical data to predict mild cognitive impairment (MCI) risk in patients with coronary heart disease (CHD). METHODS: This cross-sectional study prospectively collected data from 400 patients with coronary heart disease (aged 55-90 years, 62% men) from July 2022 to September 2023 and randomized (7:3 ratio) them into training and validation sets. After determining the modeling variables through least absolute shrinkage and selection operator regression analysis, four ML classifiers were developed: logistic regression, extreme gradient boosting (XGBoost), support vector machine, and random forest. The performance of the models was evaluated using area under the ROC curve, accuracy, sensitivity, specificity, and F1 score. Decision curve analysis was used to assess the clinical performance of the established models. The SHapley Additive exPlanations (SHAP) method was applied to determine the significance of the features, the predictive model was visualized with a nomogram, and an online web-based calculator for predicting CHD-MCI risk scores was developed. RESULTS: Of 400 CHD patients (average age 70.86 ± 8.74 years), 220 (55%) had MCI. The XGBoost model demonstrated superior performance (AUC: 0.86, accuracy: 78.57%, sensitivity: 0.74, specificity: 0.84, F1: 0.79) and underwent validation. An online tool (https://mr.cscps.com.cn/mci/index.html) with seven predictive variables (APOE gene typing, age, education, TyG index, NT-proBNP, C-reactive protein, and occupation) assessed MCI risk in CHD patients. CONCLUSION: This study highlights the potential for predicting MCI risk among CHD patients using an ML model-driven nomogram and risk scoring tool based on clinical data.

Electronic cooling and energy harvesting using ferroelectric polymer composites.

Thermal management emerges as a grand challenge of next-generation electronics. Efforts to develop compact, solid-state cooling devices have led to the exploration of the electrocaloric effect of ferroelectric polymers. Despite recent advances, the applications of electrocaloric polymers on electronics operating at elevated temperatures remain essentially unexplored. Here, we report that the ferroelectric polymer composite composed of highly-polarized barium strontium titanate nanofibers and electron-accepting [6,6] phenyl-C61-butyric acid methyl ester retains fast electrocaloric responses and stable cyclability at elevated temperatures. We demonstrate the effectiveness of electrocaloric cooling in a polymer composite for a pyroelectric energy harvesting device. The device utilizes a simulated central processing unit (CPU) as the heat source. Our results show that the device remains operational even when the CPU is overheated. Furthermore, we show that the composite functions simultaneously as a pyroelectric energy converter to harvest thermal energy from an overheated chip into electricity in the electrocaloric process. This work suggests a distinct approach for overheating protection and recycling waste heat of microelectronics.

Robust Predefined Output Containment for Heterogeneous Nonlinear Multiagent Systems Under Unknown Nonidentical Leaders' Dynamics.

This article discusses the robust predefined output containment (RPOC) control problem for heterogeneous nonlinear multiagent systems having multiple uncertain nonidentical leaders. In order to solve this problem, a new kind of distributed observer-based RPOC control framework is presented. First, for obtaining the information of nonidentical leaders' dynamics, including uncertain parameters in leaders' system matrices, output matrices, states, and outputs, four kinds of adaptive observers are constructed in a fully distributed form without any knowledge of the dynamics of nonidentical leaders, exactly. Second, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers, where the adaptive observers can make up for the uncertain parameter in followers' dynamics, and the solutions of output regulation equations can be obtained adaptively by the developed adaptive strategy. Furthermore, with the help of the output regulation method and Lyapunov stability theory, the RPOC criteria for the considered system under unknown nonidentical leaders' dynamics are derived from the constructed controller. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

Machine learning-based prediction model for the efficacy and safety of statins.

Objective: The appropriate use of statins plays a vital role in reducing the risk of atherosclerotic cardiovascular disease (ASCVD). However, due to changes in diet and lifestyle, there has been a significant increase in the number of individuals with high cholesterol levels. Therefore, it is crucial to ensure the rational use of statins. Adverse reactions associated with statins, including liver enzyme abnormalities and statin-associated muscle symptoms (SAMS), have impacted their widespread utilization. In this study, we aimed to develop a predictive model for statin efficacy and safety based on real-world clinical data using machine learning techniques. Methods: We employed various data preprocessing techniques, such as improved random forest imputation and Borderline SMOTE oversampling, to handle the dataset. Boruta method was utilized for feature selection, and the dataset was divided into training and testing sets in a 7:3 ratio. Five algorithms, including logistic regression, naive Bayes, decision tree, random forest, and gradient boosting decision tree, were used to construct the predictive models. Ten-fold cross-validation and bootstrapping sampling were performed for internal and external validation. Additionally, SHAP (SHapley Additive exPlanations) was employed for feature interpretability. Ultimately, an accessible web-based platform for predicting statin efficacy and safety was established based on the optimal predictive model. Results: The random forest algorithm exhibited the best performance among the five algorithms. The predictive models for LDL-C target attainment (AUC = 0.883, Accuracy = 0.868, Precision = 0.858, Recall = 0.863, F1 = 0.860, AUPRC = 0.906, MCC = 0.761), liver enzyme abnormalities (AUC = 0.964, Accuracy = 0.964, Precision = 0.967, Recall = 0.963, F1 = 0.965, AUPRC = 0.978, MCC = 0.938), and muscle pain/Creatine kinase (CK) abnormalities (AUC = 0.981, Accuracy = 0.980, Precision = 0.987, Recall = 0.975, F1 = 0.981, AUPRC = 0.987, MCC = 0.965) demonstrated favorable performance. The most important features of LDL-C target attainment prediction model was cerebral infarction, TG, PLT and HDL. The most important features of liver enzyme abnormalities model was CRP, CK and number of oral medications. Similarly, AST, ALT, PLT and number of oral medications were found to be important features for muscle pain/CK abnormalities. Based on the best-performing predictive model, a user-friendly web application was designed and implemented. Conclusion: This study presented a machine learning-based predictive model for statin efficacy and safety. The platform developed can assist in guiding statin therapy decisions and optimizing treatment strategies. Further research and application of the model are warranted to improve the utilization of statin therapy.

The effect of land use and land cover on soil carbon storage in the Yellow River Delta, China: Implications for wetland restoration and adaptive management.

Gaining a comprehensive understanding of the effect of land use/land cover (LULC) and soil depth on soil carbon storage, through the manipulation of external carbon input and turnover processes, is crucial for accurate predictions of regional soil carbon storage. Numerous research investigations have been conducted to examine the impact of LULC on the storage and cycling of carbon in the surface soils of coastal wetlands. Nevertheless, there remains a dearth of understanding concerning the implications of this phenomenon on subterranean soils, a crucial factor in discerning the capacity for carbon sequestration in coastal wetlands and implementing measures for their preservation. The study focused on the Yellow River Delta (YRD) in China, which serves as a representative model system. It aimed to assess the impact of LULC as well as soil depth on carbon storage. This was achieved by a combination of remote sensing interpretation and field samplings. The findings of the study indicate that there was an increase in soil organic carbon storage with both the area covered and the depth of the soil across the four different land use types, namely forest, grass, tidal flat, and cultivated land. Cultivated land was identified as the predominant LULC type, encompassing 41.73% of the entire YRD. Furthermore, it accounted for a substantial carbon storage of 76.08%. In comparison to soil layers at depths of 0-20 cm and 20-40 cm, 40-60 cm was discovered to have the maximum carbon storage, accounting for 42.29% of total carbon storage. Furthermore, one of the main factors influencing carbon storage is salinity, which shows a negative association with carbon storage. Moreover, the aforementioned findings underscore the significance of the conjoined physical and chemical properties induced by LULC in influencing the dynamics of soil carbon. This suggests that the inclusion of deep soil carbon in the estimation and restoration of soil carbon storage is necessary. This inclusion will support the realization of the United Nations' "Toward Zero Carbon" effort and facilitate the implementation of China's national carbon neutrality objectives.

Revealing the impact of modified modules flexibility on gemcitabine prodrug nanoassemblies for effective cancer therapy.

Prodrug nanoassemblies combine the advantages of prodrug strategies and nanotechnology have been widely utilized for delivering antitumor drugs. These prodrugs typically comprise active drug modules, response modules, and modification modules. Among them, the modification modules play a critical factor in improving the self-assembly ability of the parent drug. However, the impact of the specific structure of the modification modules on prodrug self-assembly remains elusive. In this study, two gemcitabine (GEM) prodrugs are developed using 2-octyl-1-dodecanol (OD) as flexible modification modules and cholesterol (CLS) as rigid modification modules. Interestingly, the differences in the chemical structure of modification modules significantly affect the assembly performance, drug release, cytotoxicity, tumor accumulation, and antitumor efficacy of prodrug nanoassemblies. It is noteworthy that the prodrug nanoassemblies constructed with flexible modifying chains (OD) exhibit improved stability, faster drug release, and enhanced antitumor effects. Our findings elucidate the significant impact of modification modules on the construction of prodrug nanoassemblies.

Urolithin A attenuates hexavalent chromium-induced small intestinal injury by modulating PP2A/Hippo/YAP1 pathway.

Hexavalent chromium (Cr(VI)) exposure has been linked with gastrointestinal toxicity, whereas the molecular pathways and key targets remain elusive. Computational toxicology analysis predicted the correlation between protein phosphatase 2A (PP2A) and genes regarding Cr(VI)-induced intestinal injury. Here, we generated a mouse model with intestinal epithelium-specific knock-out of Ppp2r1a (encoding PP2A Aα subunit) to investigate the mechanisms underlying Cr(VI)-induced small intestinal toxicity. Heterozygous mice (HE) and matched wild-type (WT) littermates were administrated with Cr(VI) at 0, 5, 20, 80 mg/L for 28 successive days. Cr(VI) treatment led to crypt hyperplasia, epithelial cell apoptosis, and intestinal barrier dysfunction, accompanied by the decline of goblet cell counts and Occludin expression in WT mice. Notably, these effects were aggravated in HE mice, indicating that PP2A Aα deficiency conferred mice with susceptibility to Cr(VI)-induced intestinal injury. Integrated data analysis and biological experiments revealed Cr(VI) exposure could decrease YAP1 phosphorylation at Ser127 but increase protein expression and activity, together with elevated TAZ protein driving epithelial crypt cells proliferation following damage, suggesting the involvement of Hippo/YAP1 signaling pathway in Cr(VI)-induced intestinal toxicity. Nevertheless, the enhanced phosphorylation of YAP1 in HE mice resulted in proliferation/repair defects in intestinal epithelium, thereby exacerbating Cr(VI)-induced gut barrier dysfunction. Notably, by molecular docking and further studies, we identified Urolithin A, a microbial metabolite, attenuated Cr(VI)-induced disruption of intestinal barrier function, partly by modulating YAP1 expression and activity. Our findings reveal the novel molecular pathways participated in Cr(VI)-caused small intestinal injury and urolithin A could potentially protect against environmental hazards-induced intestinal diseases.

Lysosomal membrane protein TMEM106B modulates hematopoietic stem and progenitor cell proliferation and differentiation by regulating LAMP2A stability.

Hematopoietic stem and progenitor cells (HSPCs) are successfully employed for hematological transplantations, and impaired HSPC function causes hematological diseases and aging. HSPCs maintain the lifelong homeostasis of blood and immune cells through continuous self-renewal and maintenance of the multilineage differentiation potential. TMEM106B is a transmembrane protein localized on lysosomal membranes and associated with neurodegenerative and cardiovascular diseases; however, its roles in HSPCs and hematopoiesis are unknown. Here, we established tmem106bb-/- knockout (KO) zebrafish and showed that tmem106bb KO reduced the proliferation of HSPCs during definitive hematopoiesis. The differentiation potential of HSPCs to lymphoid lineage was reduced, whereas the myeloid and erythroid differentiation potentials of HPSCs were increased in tmem106bb-/- zebrafish. Similar results were obtained with morpholino knockdown of tmem106bb. Mechanistically, TMEM106B interacted with LAMP2A, the lysosomal associated membrane protein 2A, impaired LAMP2A-Cathepsin A interaction, and enhanced LAMP2A stability; tmem106bb KO or TMEM106B knockdown caused LAMP2A degradation and impairment of chaperone-mediated autophagy (CMA). Knockdown of lamp2a caused similar phenotypes to that in tmem106bb-/- zebrafish, and overexpression of lamp2a rescued the impaired phenotypes of HSPCs in tmem106bb-/- embryos. These results uncover a novel molecular mechanism for the maintenance of HSPC proliferation and differentiation through stabilizing LAMP2A via TMEM106B-LAMP2A interaction.

Hereditary Colorectal Cancer and Polyposis Syndromes Caused by Variants in Uncommon Genes.

A substantial number of hereditary colorectal cancer (CRC) and colonic polyposis cannot be explained by alteration in confirmed predisposition genes, such as mismatch repair (MMR) genes, APC and MUTYH. Recently, a certain number of potential predisposition genes have been suggested, involving each a small number of cases reported so far. Here, we describe the detection of rare variants in the NTLH1, AXIN2, RNF43, BUB1, and TP53 genes in nine unrelated patients who were suspected for inherited CRC and/or colonic polyposis. Seven of them were classified as pathogenic or likely pathogenic variants (PV/LPV). Clinical manifestations of carriers were largely consistent with reported cases with, nevertheless, distinct characteristics. PV/LPV in these uncommon gene can be responsible for up to 2.7% of inherited CRC or colonic polyposis syndromes. Our findings provide supporting evidence for the role of these genes in cancer predisposition, and contribute to the determination of related cancer spectrum and cancer risk for carriers, allowing for the establishment of appropriate screening strategy and genetic counseling in affected families.

Associations of occupational exposure to micro-LiNiCoMnO2 particles with systemic inflammation and cardiac dysfunction in cathode material production for lithium batteries.

Micro-LiNiCoMnO2 (MNCM), a cathode material with highest market share, has increasing demand with the growth of lithium battery industry. However, whether MNCM exposure brings adverse effects to workers remains unclear. This study aimed to explore the association between MNCM exposure with systemic inflammation and cardiac function. A cross-sectional study of 347 workers was undertaken from the MNCM production industry in Guangdong province, China in 2020. Metals in urine were measured using ICP-MS. The associations between metals, systemic inflammation, and cardiac function were appraised using a linear or logistic regression model. Bayesian kernel machine regression (BKMR) and generalized weighted quantile sum (gWQS) models were used to explore mixed metal exposures. The analysis of interaction and mediation was adopted to assess the role of inflammation in the relation between urinary metals and cardiac function. We observed that the levels of lithium (Li) and cobalt (Co) were positively associated with systemic inflammation and heart rate. The amount of Co contributed the highest weight on the increased systemic immune-inflammation index (SII) (59.8%), the system inflammation response index (SIRI) (44.3%), and heart rate (65.0%). Based on the mediation analysis, we estimated that SII mediated 32.3% and 20.9% of the associations between Li and Co with heart rate, and SIRI mediated 44.6% and 22.2% of the associations between Li and Co with heart rate, respectively. This study demonstrated for the first time that MNCM exposure increased the risk of workers' systemic inflammation and elevated heart rate, which were contributed by the excessive Li and Co exposure. Additionally, it indicates that systemic inflammation was a major mediator of the associations of Li and Co with cardiac function in MNCM production workers.

The link between patient safety competence and adverse event among master of nursing students: a cross-sectional mixed-methods study.

BACKGROUND: Patient safety (PS) is a core competency for registered nurses. However, there is a gap between the PS competence of nursing students and their clinical experience in PS. This study explored the effect of PS competence levels on the occurrence of adverse events (AEs) among nursing master's students in China. METHODS: A sequential mixed methods design was used, with a purposive sample across seven colleges. A total of 327 graduate nursing students, aged 22 to 38, participated in the survey, and 15 participated in qualitative interviews. The Health Professional Education in Patient Safety Survey (H-PEPSS) assessed the students' competence levels in PS. The respondents also reported any AEs that they had been involved in over the past year. RESULTS: A total of 78 AEs occurred in the past year, with 17.7% of the participants involved 1 to 3 AEs. The most common AEs were medication administration errors (30.77%) and improper use of medical equipment/supplies (28.20%). Students acquired more competencies from the clinical setting than from the classroom setting. Three competencies learned from classroom settings were associated with clinical AEs: low clinical safety skills [OR = 0.61], inappropriate identify, response to and disclosing AE and close calls [OR = 0.454], and low confidence in working in teams with other health professionals [OR = 2.168]. Qualitative data analysis revealed five themes: recognizing AEs, reducing harm by addressing immediate risks to patients and others involved, promoting safe medication and clinical practice, managing members' authority and team dynamics, and dealing with inter-professional conflict. CONCLUSIONS: The quantitative and qualitative data align, supporting the enhancement of students' PS competence.

Ethical risks in robot health education: A qualitative study.

BACKGROUND: As health education robots may potentially become a significant support force in nursing practice in the future, it is imperative to adhere to the European Union's concept of "Responsible Research and Innovation" (RRI) and deeply reflect on the ethical risks hidden in the process of intelligent robotic health education. AIM: This study explores the perceptions of professional nursing professionals regarding the potential ethical risks associated with the clinical practice of intelligent robotic health education. RESEARCH DESIGN: This study adopts a descriptive phenomenological approach, employing Colaizzi's seven-step method for data analysis. PARTICIPANTS AND RESEARCH CONTEXT: We conducted semi-structured interviews with 17 nursing professionals from tertiary comprehensive hospitals in China. ETHICAL CONSIDERATIONS: This study has been approved by the Ethics Committee of the Second Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Provincial Second Chinese Medicine Hospital. FINDINGS: Nursing personnel, adhering to the principles of RRI and the concept of "person-centered" care, have critically reflected on the potential ethical risks inherent in robotic health education. This reflection has primarily identified six themes: (a) threats to human dignity, (b) concerns about patient safety, (c) apprehensions about privacy disclosure, (d) worries about implicit burdens, (e) concerns about responsibility attribution, and (f) expectations for social support. CONCLUSIONS: This study focuses on health education robots, which are perceived to have minimal ethical risks, and provides rich and detailed insights into the ethical risks associated with robotic health education. Even seemingly safe health education robots elicit significant concerns among professionals regarding their safety and ethics in clinical practice. As we move forward, it is essential to remain attentive to the potential negative impacts of robots and actively address them.

Syntheses and biological activities of calix[4]resorcinarene derivatives modified by sulfonic acid and sulfonamides.

Functionalization of C-propyl-resorcinolcalix[4]arene (1a) and C-iso-butyl-resorcinolcalix[4]arene (1b) with sodium sulfite and formaldehyde solution gave two corresponding sulfonatomethylated calix[4]resorcinarenes 2a/b. Further modification of 2a/b with different primary amines afforded three calix[4]resorcinarene sulfonamides 3a/b and 4c. Antibacterial and antitumor tests were performed on the starting calix[4]resorcinarenes and their sulfonic acid and sulfonamide derivatives. The results showed that in terms of antimicrobial activity calix[4]resorcinarenes and their derivatives showed bacteriostatic activity against both Escherichia coli and Staphylococcus aureus. Of which compound 1b was the most effective against Escherichia coli with a MIC value of 6.25 mg mL; compound 2b was the most effective against Staphylococcus aureus with a MIC value of 3.12 mg mL-1. In terms of antitumor activity, calix[4]resorcinarenes and their derivatives showed inhibitory effects on the three tumor cells selected for the experiment. Among them, the survival rate of A549 was 76.03% in the presence of 40 µM 1b, and the survival rates of HepG2 and MDA-MB-321 were 28.66% and 65.39% in the presence of 40 µM 2b, respectively.

Deep Learning and Histogram-Based Grain Size Analysis of Images.

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering.

Closed-Loop Control of Melt Pool Temperature during Laser Metal Deposition.

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller.