Self-Adaptive Photodynamic-to-Photothermal Switch for Smart Antitumor Photoimmunotherapy.

Photodynamic therapy (PDT) and photothermal therapy (PTT) possess different merits in cancer phototherapy, but the tumor microenvironment becomes unfavorable during the phototheranostic progress. Herein, we report a self-adaptive cyanine derivative Cy5-TPA with the PDT-dominated state to PTT-dominated state autoswitch feature for enhanced photoimmunotherapy. The incorporation of rotatable triphenylamine (TPA) moiety renders Cy5-TPA with the temperature or intramolecular-motion regulated photoactivities, which shows preferable reactive oxygen species (ROS) generation at lower temperature while stronger photothermal conversion at higher ones. Such a promising feature permits the in situ switch from PDT-dominated state to PTT-dominated state along with intratumoral temperature increase during laser irradiation, which also works in line with the concurrently reduced intratumoral oxygen level, exhibiting a self-adaptive phototherapeutic behavior to maximize the phototherapeutic antitumor outcome. Most importantly, the self-adaptive PDT-dominated state to PTT-dominated state switch also facilitates the sequential generation and release of damage-associated molecular patterns during immunogenic cell death (ICD). Hence, Cy5-TPA demonstrates excellent photoimmunotherapy performance in ICD induction, dendritic cell maturation, and T cell activation for tumor eradication and metastasis inhibition.

An improved diagnostic method for taurodontism and a comparative study on its effectiveness evaluation.

OBJECTIVE: The two commonly used diagnostic methods for taurodontism are susceptible to aging changes, mastication wear and other factors. Therefore, this study proposed an improved diagnostic method for taurodontism, and compared it with the previous two methods as a supplement for taurodontism diagnosis. METHODS: The included patients were aged 10-89 years and admitted to the Department of Stomatology of Hebei Eye Hospital from June 1, 2022 to May 31, 2023. Eighty cone-beam computed tomography images were divided equally into 4 groups: 10-29, 30-49, 50-69, and 70-89 years old. The right mandibular first molars were selected as measurement objects. Firstly, |BD| and taurodontism index (TI)-related parameters were measured using Shifman and Chanannel's method and crown-body(CB) and root (R) lengths was measured by Seow and Lai's method. The improved method used the length from the cementoenamel junction(CEJ) to the root bifurcation point(body, B)and the root length(root, R)as the measurement objects. Finally, TI, CB/R ratios, and B/R ratios were calculated according to the formulas given below. One-way ANOVA analysis was mainly used to compare the differences in the values, indices and ratios of taurodontism among different age groups (p<0.05). RESULTS: With the increase of age, |BD| and TI values decreased significantly (p<0.01). The CB/R ratios of 70-89 years group were significantly lower than those of the other three groups (p<0.01). Ratios derived from the improved method were significantly lower in the 70-89 years than in 10-29 years group (p<0.05). CONCLUSIONS: The |BD| and TI parameters proposed by Shifman and channel are significantly influenced by age. The measurements of Seow and Lai (CB/R ratios) were less affected by age compared with those of the former. The improved method(B/R ratios) was least affected by age, which would reduce error and bias in the measurement of taurodontism and obtain more objective results in older patients.

Study on the development characteristics and spatial and temporal patterns of population ageing in 31 central cities in China.

Background: Population ageing is inseparable from technological innovation, social progress and the development of human civilization, and constitutes a new element in the development of contemporary human history. Objective: To dynamically analyses the developmental, structural and growth characteristics of population ageing in 31 provincial capitals and municipalities in China, using the data of the fifth national census in 2000 and the seventh national census in 2020. Methods: The development characteristics and spatial and temporal patterns of population aging in the 31 cities were measured using the population aging index growth model, Theil's index, coefficient of variation, population aging index and other analytical methods. Results: (1) From 2000 to 2020, the population aging rate of the 31 central cities generally increased, and the population aging level of the cities showed the characteristics of "East-Central-Northeast-West" to "Northeast-East-Central-West" decreasing. (2) Regional differences in the ratio of old to young are relatively high, while regional differences in the level of population ageing are relatively small. The level of population ageing is classified with the indicators of size structure, family structure and age structure in the first and third quadrants, and with the geographic concentration rate in the second and fourth quadrants. (3) China's population ageing has a T-shaped spatial distribution characteristic pointing along the coast - along the Yangtze Rivers. Conclusion: The 31 central cities are the center of gravity of China's economy and have strong economic power in dealing with the challenges of population ageing, but how to make population ageing compatible with the economy and society, and then promote sustainable population development, is a topic that needs further attention in the study.

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH. METHODS: The data utilized in this study were collected from neonatology departments of four tertiary-level hospitals in China. To address the issue of imbalanced data, oversampling algorithms synthetic minority over-sampling technique (SMOTE) was applied to improve the model. RESULTS: Seven hundred sixty one clinical records were collected in our study. Following data pre-processing and feature selection, 5 of the 46 features were used to build models, including duration of invasive respiratory support (day), the severity of BPD, ventilator-associated pneumonia, pulmonary hemorrhage, and early-onset PH. Four machine learning models were applied to predictive learning, and after comprehensive selection a model was ultimately selected. The model achieved 93.8% sensitivity, 85.0% accuracy, and 0.933 AUC. A score of the logistic regression formula greater than 0 was identified as a warning sign of BPD-PH. CONCLUSIONS: We comprehensively compared different machine learning models and ultimately obtained a good prognosis model which was sufficient to support pediatric clinicians to make early diagnosis and formulate a better treatment plan for pediatric patients with BPD-PH.

YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION: In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.

Vaginal micro-environment disorder promotes malignant prognosis of low-grade cervical intraepithelial neoplasia: a prospective community cohort study in Shanxi Province, China.

PURPOSE: Emerging evidence suggests that vaginal micro-environment disorder is closely related to the development of cervical lesions. Low-grade cervical intraepithelial neoplasia (CIN1), as an early stage of cervical lesions, exhibits a high risk of progressing to high-grade lesions or even cervical cancer. However, the effect of vaginal micro-environment on the malignant prognosis of CIN1 remains uncertain. METHODS: A total of 504 patients diagnosed with CIN1 by pathology, who were from the population-based cohorts established in Shanxi Province, China, were enrolled and followed up for 2 years. Micro-environmental factors such as vaginal pH, cleanliness, hydrogen peroxide (H2O2), ß-glucuronidase (GUSB), leucocyte esterase (LE), and sialidase (SNA) were detected to evaluate their effect on the malignant prognosis of CIN1. RESULTS: Abnormal vaginal pH (HR = 1.472, 95%CI 1.071-2.022), cleanliness (HR = 1.446, 95%CI 1.067-1.960), H2O2 (HR = 1.525, 95%CI 1.155-2.013), GUSB (HR = 1.739, 95%CI 1.235-2.448), LE (HR = 1.434, 95%CI 1.038-1.981), and SNA (HR = 1.411, 95%CI 1.065-1.870) could promote a higher incidence of CIN1 malignant prognosis, and the combined effects of these micro-environmental factors resulted in a nearly twofold increased risk (HR = 2.492, 95%CI 1.773-3.504) compared to any single factor alone, especially under the high-risk human papillomavirus (HR-HPV) infection. Notably, the cumulative incidence of malignant prognosis for CIN1 gradually increased during the early follow-up period, reaching its peak at approximately 8 months, and then stabilizing. CONCLUSION: Vaginal micro-environment disorder could promote CIN1 malignant prognosis, particularly in HR-HPV-infected women. Taking micro-environmental factors as the breakthrough, our study provides a feasible vision for preventing early stage cervical lesions.

A carbon dot-based time-dependent color-changing room temperature phosphorescent material with facile synthesis.

Carbon dots have attracted widespread attention due to their excellent optical properties and so on and are therefore used in various fields such as anti-counterfeiting. There are many reports on carbon dot-based room-temperature phosphorescent materials, but there are still fewer reports on carbon dot-based room-temperature phosphorescent materials with time-dependent color-changing properties. In this work, a time-dependent color-changing carbon dot-based room-temperature phosphorescent material with the ability to change from green to blue was successfully prepared by a simple one-pot heating method using hydroxyurea as the only raw material. In this process, hydroxyurea is used as both a carbon and nitrogen source, and in the process of material formation, hydroxyurea also partially forms cyanuric acid as a matrix to make the carbon dots uniformly dispersed in it. By blending the ratio of the dual emission centers of the carbon dots themselves, the final effect of time-dependent color-changing is achieved by taking advantage of the intensity changes and color differences of each emission center. The present work provides new ideas for the preparation of time-dependent color-changing carbon dot-based room-temperature phosphorescent materials.

Enhanced Aquathermolysis of Water-Heavy Oil-Ethanol Catalyzed by B@Zn(II)L at Low Temperature.

In order to study the synergistic effects of exogenous catalysts and in situ minerals in the reservoir during heavy oil aquathermolysis, in this paper, a series of simple supported transition metal complexes were prepared using sodium citrate, chloride salts and bentonite, and their catalytic viscosity reduction performances for heavy oil were investigated. Bentonite complex catalyst marked as B@Zn(II)L appears to be the most effective complex. B@Zn(II)L was characterized by scanning electron microscopy (SEM), Fourier-Transform Infrared (FTIR) spectroscopy, thermo-gravimetric analysis (TGA) and N2 adsorption-desorption isotherms. Under optimized conditions, the viscosity of the heavy oil was decreased by 88.3%. The reaction temperature was reduced by about 70 °C compared with the traditional reaction. The results of the group composition analysis and the elemental content of the heavy oil indicate that the resin and asphaltene content decreases, and the saturated and aromatic HC content increases. The results of TGA and DSC of the heavy oil show that the macromolecular substances in the heavy oil were cracked into small molecules with low boiling points by the reaction. GC-MS examination of water-soluble polar compounds post-reaction indicates that B@Zn(II)L can diminish the quantity of polar substances in heavy oil and lower the aromatic nature of these compounds. Thiophene and quinoline were utilized as model compounds to investigate the reaction mechanism. GC-MS analysis revealed that C-C, C-N and C-S bonds were cleaved during the reaction, leading to a decrease in the viscosity of heavy oil.

Screening for ferroptosis genes related to endometrial carcinoma and predicting of targeted drugs based on bioinformatics.

Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG_CELL_CYCLE and KEGG_OOCYTE_MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.

BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. METHODS: A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. RESULTS: Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. CONCLUSION: This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Exposure to polycyclic aromatic hydrocarbons promotes the progression of low-grade cervical intraepithelial neoplasia: A population-based cohort study in China.

Low-grade cervical intraepithelial neoplasia (CIN1) is an early stage of cervical cancer development. Previously, we reported that exposure to polycyclic aromatic hydrocarbons (PAHs) increases the risk of cervical precancerous lesions, especially in females with a high-risk human papillomavirus (HR-HPV) infection. However, the effects of PAHs on CIN1 progression remain unclear. A community-based prospective cohort study was conducted to evaluate the role of exposure to PAHs in the progression of CIN1. A total of 564 patients diagnosed with CIN1 were followed-up at 6, 12, and 24 months, post-diagnosis, to determine CIN1 reversion, persistence, and progression. Exposure to PAHs was determined by the urine 1-hydroxipayrene (1-OHP) level. Our results showed that the 1-OHP level was significantly higher in patients with CIN1 persistence/progression than in those with reversion (P < .05). High exposure to PAHs increased the risk of CIN1 persistence/progression, with hazard ratios (HR), 95% confidence intervals (CI) of (1.62, 1.24-2.67), (1.98, 1.42-2.75), and (2.37, 1.61-3.49) at 6, 12, and 24 months, post-diagnosis, respectively. The effect was enhanced with HR-HPV positivity, as determined at 6 (1.82, 1.24-2.67), 12 (3.02, 1.74-5.23), and 24 (2.51, 1.48-4.26) months, post-diagnosis. Moreover, the predictive value of exposure to PAHs for CIN1 persistence/progression was higher in HR-HPV-positive patients than in HR-HPV-negative patients. The results revealed that exposure to PAHs facilitated the malignant progression of CIN1 and hindered its reversal, particularly in patients with HR-HPV infection. Our findings provide novel insights into early prevention and intervention targeting the initiation and progression of cervical neoplasia.

Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention Mechanisms.

To address the various challenges in aluminum surface defect detection, such as multiscale intricacies, sensitivity to lighting variations, occlusion, and noise, this study proposes the AluDef-ClassNet model. Firstly, a Gaussian difference pyramid is utilized to capture multiscale image features. Secondly, a self-attention mechanism is introduced to enhance feature representation. Additionally, an improved residual network structure incorporating dilated convolutions is adopted to increase the receptive field, thereby enhancing the network's ability to learn from extensive information. A small-scale dataset of high-quality aluminum surface defect images is acquired using a CCD camera. To better tackle the challenges in surface defect detection, advanced deep learning techniques and data augmentation strategies are employed. To address the difficulty of data labeling, a transfer learning approach based on fine-tuning is utilized, leveraging prior knowledge to enhance the efficiency and accuracy of model training. In dataset testing, our model achieved a classification accuracy of 97.6%, demonstrating significant advantages over other classification models.

Treatment of electroplating wastewater using electrocoagulation and integrated membrane.

Electroplating wastewater contains heavy metal ions and organic matter. These contaminants not only endanger the environment but also pose risks to human health. Despite the development of various treatment processes such as chemical precipitation MBR, electrocoagulation (EC) ceramic membrane (CM), coagulation ultrafiltration (UF) reverse osmosis (RO), and CM RO. These methods are only effective for low concentrations of heavy metals and struggle with high concentrations. To address the challenge of treating electroplating wastewater with high heavy metal content, this study focuses on the wastewater from Dongfang Aviation Machinery Processing Plant. It introduces an EC and integrated membrane (IM) treatment process for electroplating wastewater. The IM comprises microfiltration (MF) membrane, nanofiltration (NF) membrane, and RO membrane. Results indicated that under specific conditions, such as a pH of 8, current density of 5 A/dm2, electrode plate spacing of 2 cm, 35 min of electrolysis time, and influent pH of 10 for the IM, removal rates of Zn2+, Cu2+, Ni2+, and TCr in the wastewater exceeded 99%. The removal rates of chemical oxygen demand (COD), suspended solids (SS), total phosphorus (TP), total nitrogen (TN), and petroleum in wastewater exceed 97%. Following a continuous cleaning process, the membrane flux can consistently recover to over 94.3%.

B-lymphocyte-induced maturation protein-1 inhibits inflammation and pyroptosis to alleviate sepsis injury.

Liver and lung tissue damage caused by sepsis is still one of the causes of death. B-lymphocyte-induced maturation protein-1 (Blimp-1) has a protective role in inflammation-related disease. However, whether Blimp-1 can regulate cell pyroptosis and affect disease progression in sepsis is still unclear. Animal and cell models were established by the cecal ligation and puncture method and lipopolysaccharides (LPS)-induced RAW 264.7 cells, respectively, and the role of Blimp-1 in regulation inflammatory response and pyroptosis was verified. The changes of inflammation and pyroptosis in liver and lung tissues of septic mice were determined by the addition of TAK-242 (TLR4 inhibitor). Cell pyroptosis and the level of inflammation was detected after Blimp-1 knockdown and TAK-242 treatment in the cell model. The expression of Blimp-1 was continuously increased in a septic mice model. After treatment with TAK-242, the expression of Blimp-1, pyroptosis and inflammatory levels were reduced in mice. In the LPS-induced cell model, cell injury by knockout Blimp-1 was increased, and cell activity was restored after TAK-242 intervention. Overexpression of Blimp-1 relieved LPS-induced cellular inflammatory damage and pyroptosis. Our study had shown that Blimp-1 could improve septic damage by regulating the level of cellular inflammation and pyroptosis in sepsis.

Chemical and linguistic considerations for encoding Chinese characters: an embodiment using chain-end degradable sequence-defined oligourethanes created by consecutive solid phase click chemistry.

Sequence-defined polymers (SDPs) are currently being investigated for use as information storage media. As the number of monomers in the SDPs increases, with a corresponding increase in mathematical base, the use of tandem-MS for de novo sequencing becomes more challenging. In contrast, chain-end degradation routines are truly de novo, potentially allowing very large mathematical bases for encoding. While alphabetic scripts have a few dozen symbols, logographic scripts, such as Chinese, can have several thousand symbols. Using a new in situ consecutive click reaction approach on an oligourethane backbone for writing, and a previously reported chain-end degradation routine for reading, we encoded/decoded a confucius proverb written in Chinese characters using two encoding schemes: Unicode and Zhèng Ma. Unicode is an internationally standardized arbitrary string of hexadecimal (base-16) symbols which efficiently encodes uniquely identifiable symbols but requires complete fidelity of transmission, or context-based inferential strategies to be interpreted. The Zhèng Ma approach encodes with a base-26 system using the visual characteristics and internal composition of Chinese characters themselves, which leads to greater ambiguity of encoded strings, but more robust retrievability of information from partial or corrupted encodings. The application of information-encoded oligourethanes to two different encoding systems allowed us to establish their flexibility and versatility for data storage. We found the oligourethanes immensely adaptable to both encoding schemes for Chinese characters, and we highlight the expected tradeoff between the efficiency and uniqueness of Unicode encoding on the one hand, and the fidelity to a scripts' particular visual characteristics on the other.

IgLON5 autoimmunity in a patient with Creutzfeldt-Jakob disease: case report and review of literature.

Objective: We present the case of a patient with clinical and imaging features of sporadic Creutzfeldt-Jakob disease (sCJD) and positive IgLON5 antibodies (Abs) in the serum and CSF. Case report: A 66-year-old Chinese man presented to the hospital with a stroke-like episode, followed by rapidly progressive cognitive decline, mutism, and parkinsonism. The MRI results showed a cortical ribboning sign in diffusion-weighted MRI, periodic triphasic waves with a slow background in EEG, and positive protein 14-3-3 in CSF. There were matching IgLON5 Abs in the serum and CSF. A literature review showed positive autoimmune encephalitis Abs or autoimmune inflammatory disease between 0.5 and 8.6% among patients with clinical suspicion of CJD, most commonly anti-voltage-gated potassium channel (VGKC) complex and anti-N-methyl-D-aspartate receptor (NMDAR) Abs; however, IgLON5 autoimmunity in CJD has been rarely reported. This is an intriguing association as both conditions have been associated with brain deposits of phosphorylated tau protein. Conclusion: IgLON5 Abs may be observed in patients with a diagnosis of CJD; it is unknown whether a synergistic effect of IgLON5 Abs with CJD exists, increasing neurodegenerative changes.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

The correlation between iodine and metabolism: a review.

Iodine is involved in the synthesis of thyroid hormones and plays a crucial role in human life. Both iodine deficiency and excess are common issues in certain populations. Iodine also has extrathyroidal effects on organs that can uptake it independently of thyroid hormones. Recently, multiple clinical studies have shown a connection between iodine intake and metabolic disorders, such as metabolic syndrome, obesity, diabetes, hypertension, and dyslipidemia. However, the results of these studies have been inconsistent, and the mechanisms behind these associations are still not well understood. Therefore, in this review, we aim to examine the recent research progress regarding the relationship between iodine and metabolic disorders, along with the relevant mechanisms.

Portable ratiometric fluorescence detection of Cu2+and thiram.

Food contaminants pose a danger to human health, but rapid, sensitive and reliable food safety detection methods can offer a solution to this problem. In this study, an optical fiber ratiometric fluorescence sensing system based on carbon dots (CDs) and o-phenylenediamine (OPD) was constructed. The ratiometric fluorescence response of Cu2+and thiram was carried out by the fluorescence resonance energy transfer (FRET) between CDs and 2,3-diaminophenazine (ox-OPD, oxidized state o-phenylenediamine). The oxidation of OPD by Cu2+resulted in the formation of ox-OPD, which quenched the fluorescence of CDs and exhibited a new emission peak at 573 nm. The formation of a [dithiocarbamate-Cu2+] (DTC-Cu2+) complex by reacting thiram with Cu2+, inhibits the OPD oxidation reaction triggered by Cu2+, thus turning off the fluorescence signal of OPD-Cu2+. The as-established detection system presented excellent sensitivity and selectivity for the detection of Cu2+and thiram in the ranges of 1 ∼ 100µM and 5 ∼ 50µM, respectively. The lowest detection limits were 0.392µM for Cu2+and 0.522µM for thiram. Furthermore, actual sample analysis indicated that the sensor had the potential for Cu2+and thiram assays in real sample analysis.