A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings. Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists. Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024. Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation. Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE). Results: The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001). Conclusions and Relevance: In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. METHODS: Using the open-source dataset MIMIC-IV and MIMICCXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. RESULTS: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. CONCLUSION: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments.

Vitamin D alleviation of oxidative stress in human retinal pigment epithelial cells.

BACKGROUND: Oxidative stress-induced retinal pigment epithelium (RPE) cell damage is a major factor in age-related macular degeneration (AMD). Vitamin D3 (VD3) is a powerful antioxidant and it has been suggested to have anti-aging properties and potential for treating AMD. This study aimed to investigate the effect of VD3 on RPE cell oxidative apoptosis of RPE cells in order to provide experimental evidence for the treatment of AMD. METHODS: Human retinal pigment epithelial cell 19 (ARPE-19) cells were divided into four groups: blank group (untreated), model group (incubated in medium with 400 µmol/L H2O2 for 1 h), VD3 group (incubated in medium with 100 µmol/L VD3 for 24 h), and treatment group (incubated in medium with 400 µmol/L H2O2 for 1 h and 100 µmol/L VD3 for 24 h). Cell viability, cell senescence, ROS content, expression levels of vitamin D specific receptors, Akt, Sirt1, NAMPT, and JNK mRNA expression levels, SOD activity, and MDA, GSH, and GPX levels were measured. RESULTS: We first established an ARPE-19 cell stress model with H2O2. Our control experiment showed that VD3 treatment had no significant effect on ARPE-19 cell viability within 6-48 h. Treating the stressed ARPE-19 cells with VD3 showed mixed results; caspase-3 expression was decreased, Bcl-2 expression was increased, MDA level of ARPE-19 cells was decreased, GSH-PX, GPX and SOD levels were increased, the relative mRNA expression levels of Akt, Sirt1, NAMPT were increased (P < 0.05), and the relative mRNA expression level of JNK was decreased (P < 0.05). CONCLUSION: VD3 can potentially slow the development of AMD.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Sex-specific association between carotid atherosclerosis and fundus arteriosclerosis in a Chinese population: a retrospective cross-sectional study.

OBJECTIVES: Vascular stiffening is highly predictive of major adverse cardiovascular events. It is not clear whether microangiopathy, such as fundus arteriosclerosis, is related to carotid atherosclerosis. Hence, this study was designed to investigate the relationship between carotid atherosclerosis and fundus arteriosclerosis among individuals of different sexes in the Chinese health-examination population. METHODS: This retrospective cross-sectional study involved 20,836 participants, including 13050 males and 7786 females. All participants underwent a detailed health examination, including medical history assessment, physical examination, assessment of lifestyle factors, fundus photography, Doppler ultrasound examination of the neck, and laboratory examinations. Two trained ophthalmologists analysed fundus arteriosclerosis based on fundus photographs, while carotid atherosclerosis was diagnosed using colour Doppler sonography of the neck. Binary logistic regression was used to analyse the relationship between carotid atherosclerosis and fundus arteriosclerosis. RESULTS: In participants with fundus arteriosclerosis, the incidence of carotid atherosclerosis was higher than that of participants without fundus arteriosclerosis (52.94% vs. 47.06%). After adjustments for potential confounding factors, fundus arteriosclerosis was significantly associated with the risk of carotid atherosclerosis. The OR with 95% CI for fundus arteriosclerosis was 1.17 (1.02, 1.34) with p = 0.0262, and individuals who did not have fundus arteriosclerosis were used as a reference in the total population. Fundus arteriosclerosis was associated with the incidence of carotid atherosclerosis in males (p = 0.0005) but not in females (p = 0.0746). CONCLUSIONS: Fundus arteriosclerosis was closely associated with carotid atherosclerosis in the Chinese population. This association was found in males but not in females.

Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records.

The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation. Deep learning models can accurately identify AFs if large amounts of annotated data are available for model training. However, it is impractical to request sufficient labels for ECG recordings for an individual patient to train a personalized model. We propose a Siamese-network-based approach for transfer learning to address this issue. A pre-trained Siamese convolutional neural network is created by comparing two labeled ECG segments from the same patient. We sampled 30-second ECG segments with a 50% overlapping window from the ECG recordings of patients in the MIT-BIH Atrial Fibrillation Database. Subsequently, we independently detected the occurrence of AF in each patient in the Long-Term AF Database. By fine-tuning the model with the 1, 3, 5, 7, 9, or 11 ECG segments ranging from 30 to 180 s, our method achieved macro-F1 scores of 96.84%, 96.91%, 96.97%, 97.02%, 97.05%, and 97.07%, respectively.

Spontaneous immunomodulation and regulation of angiogenesis and osteogenesis by Sr/Cu-borosilicate glass (BSG) bone cement to repair critical bone defects.

Injectable bone biomaterials like bone cement should be designed and fabricated with certain biological criteria, which include: 1) recruitment and polarization of the macrophages from M1 (pro-inflammatory) to M2 (anti-inflammatory) phenotype, 2) enhance vascularization, and 3) activate osteogenic differentiation of bone marrow-derived stem cells to promote bone healing. So far, no injectable biomaterials could spontaneously regulate the entire bone healing process that involves inflammation, angiogenesis, and osteogenesis. Therefore, in this study, we designed bone cement comprised of strontium and copper-incorporated borosilicate glass (Sr/Cu-BSG) in the liquid phase of chitosan to modulate bone healing. In vitro studies showed that the controlled release of Sr and Cu ions up-regulated anti-inflammatory genes(IL-1Ra and TGF-ß1) while down-regulating pro-inflammatory genes(IL-1ß and IL-6) in macrophages at 3 days. Sr and Cu ions also increased the expressions of angiogenic genes (VEGF and bFGF) in HUVECs at 5 days and osteogenic genes (Runx-2, OCN, and OPN) in hBMSCs at 7, 14, and 21 days. 5Sr3Cu-BSG bone cement exhibited the best anti-inflammatory, angiogenic, and osteogenic properties among the bone cement groups with different Sr and Cu ratios. Short-term and long-term implantation of Sr/Cu-BSGs in femoral condylar bone defects of rats and rabbits confirmed the in vitro results, where the degradation rate of Sr/Cu-BSG matched the bone healing rate. Similar to in vitro, the 5Sr3Cu-BSG group also showed the highest bone formation in vivo. Excellent physical and chemical properties, along with its bone repairing ability, make the Sr/Cu-BSG bone cement a good candidate biomaterial for treating bone defects.

Retinal vascular fractal dimension measurements in patients with obstructive sleep apnea syndrome: a retrospective case-control study.

STUDY OBJECTIVES: We performed a case-control study to investigate the correlation between the apnea-hypopnea index (AHI) and the retinal vascular fractal dimension (FD). METHODS: We selected 527 individuals who underwent polysomnography during health checkups at the Huadong Sanatorium from January to December 2021 as the study population, of whom 468 were included and 59 were excluded. All participants underwent a detailed health examination, including medical history assessment, physical examination, assessment of lifestyle factors, fundus photography, and laboratory examinations. The retinal vasculature was quantitatively assessed using Singapore I Vessel Assessment (SIVA) software. The relationship between the AHI and the retinal vessel quantitative was examined by multiple linear regression analyses and restricted cubic spline. RESULTS: Among the 468 studied individuals, the average age was 51.51 (43-58) years, with 369 (78.85%) men and 99 (21.15%) women. According to the AHI indicator, 355 individuals were diagnosed with obstructive sleep apnea (OSA) syndrome, with an average AHI of 17.00 (9.200-30.130) events/h; 113 individuals were classified as controls, with an average AHI of 2.13 (0.88-3.63) events/h. In multiple linear regression, following varying degrees of adjustment for confounding factors, FD was reduced by 0.013 (P = .012; 95% confidence interval [CI]: -0.024 to -0.003), FD arteriole (FDa) was reduced by 0.013 (P = .019; 95% CI: -0.024 to -0.002), and FD venule (FDv) was reduced by 0.014 (P = .08; 95% CI: -0.024 to -0.004) in the high-AHI group compared with the low-AHI group. All tests for trend P values were < .05. The restricted cubic spline in the overall OSA population and the individuals without diabetes revealed a U-shaped pattern of decreasing, then increasing, FD, FDa, and FDv with a rising AHI. In the OSA individual with diabetes, FD, FDa, and FDv gradually decreased with increasing AHI. CONCLUSIONS: The FD is associated with AHI in OSA individuals. The link between AHI and FD varied for OSA individuals with and without diabetes. CITATION: Wang J, Chen T, Qi X, Li Y, Yang X, Meng X. Retinal vascular fractal dimension measurements in patients with obstructive sleep apnea syndrome: a retrospective case-control study. J Clin Sleep Med. 2023;19(3):479-490.

Chinese familial central precocious puberty with hyperuricemia due to recurrent DLK1 mutation: Case report and review of the literature.

BACKGROUND: Central precocious puberty (CPP) is a precocious puberty due to premature activation of the hypothalamic-pituitary-gonadal axis (HPG). MKRN3 defects are well-known causes of CPP, while DLK1 mutations were recently identified in a few patients with CPP. METHODS: The study was approved by the Institutional Review and the scientific committee of the hospital. The clinical data were collected. Whole-exome sequencing (WES) was performed to detect causative variants. Key words 'DLK1', 'MKRN3', and "central precocious puberty" were used for literature search in PubMed, Google Scholar, HGMD, and OMIM databases. RESULTS: The patient, a male, whose puberty began before age nine, had significant metabolic abnormalities including overweight, hyperlipidemia, and hyperuricemia. WES detected a recurrent frame-shift mutation, NM_003836.5:c.479delC(p.P160fs*50) in DLK1 in the patient and his father. CONCLUSION: The familial DLK1-CPP was identified in China for the first time, which supported that short stature is predicted in patients with CPP without GnRHa treatment. Therefore, we recommend that children with DLK1-CPP should be treated as early as possible to improve adult height. The patient in this study had persistent hyperuricemia, further suggests that this antiadipogenic factor represents a link between reproduction and metabolism.

Changes in retinal vascular bifurcation in eyes with myopia.

OBJECTIVE: To evaluate the effect of myopia on retinal vascular bifurcation. METHODS: A cross-sectional study that retrospectively analyzed the fundus photographs and clinical data of 493 people who participated in routine physical examinations in Huadong Sanatorium. One eye of each subject was included in the analysis. Retinal vascular bifurcation measurements were extracted by using a validated computer program. One-way ANOVA and analysis of covariance were performed to compare the measurements across high myopia, low to moderate myopia, and non-myopia groups. RESULTS: The mean age was 41.83 ± 10.43 years and 63.49% were women. The mean spherical equivalent refraction (SER) was - 4.59 ± 3.07 D. Ninety-nine (20.08%) eyes met the definition of high myopia (SER ≤ -6.0 D), along with 234 (47.46%) low to moderate myopia (-6.0 D < SER <-0.5 D), and 160 (32.45%) non-myopia (SER ≥ -0.5 D). The differences in the arteriolar branching angle, venular branching coefficient, venular asymmetry ratio, venular angular asymmetry, and venular junctional exponent among the three groups remained significant (p < 0.05) after multivariate adjustment. Pairwise comparisons showed arteriolar branching angle and venular angular asymmetry in high myopia were significantly lower than low to moderate myopia (p < 0.001, p = 0.014 respectively) and non-myopia (p = 0.007, p = 0.048 respectively). Venular asymmetry ratio and venular branching coefficient in high myopia were significantly higher than low to moderate myopia (p = 0.029, p = 0.001 respectively) and non-myopia (p = 0.041, p = 0.043 respectively). There was a significant difference in venular junctional exponent between high myopia and low to moderate myopia (p = 0.031). CONCLUSION: The vascular bifurcation differs in dependence on the myopic refractive error and a significant increase in the difference can be observed in high myopic eyes.

Manipulating Bacterial Biofilms Using Materiobiology and Synthetic Biology Approaches.

Bacteria form biofilms on material surfaces within hours. Biofilms are often considered problematic substances in the fields such as biomedical devices and the food industry; however, they are beneficial in other fields such as fermentation, water remediation, and civil engineering. Biofilm properties depend on their genome and the extracellular environment, including pH, shear stress, and matrices topography, stiffness, wettability, and charges during biofilm formation. These surface properties have feedback effects on biofilm formation at different stages. Due to emerging technology such as synthetic biology and genome editing, many studies have focused on functionalizing biofilm for specific applications. Nevertheless, few studies combine these two approaches to produce or modify biofilms. This review summarizes up-to-date materials science and synthetic biology approaches to controlling biofilms. The review proposed a potential research direction in the future that can gain better control of bacteria and biofilms.

Unraveling Hidden Major Factors by Breaking Heterogeneity into Homogeneous Parts within Many-System Problems.

For a large ensemble of complex systems, a Many-System Problem (MSP) studies how heterogeneity constrains and hides structural mechanisms, and how to uncover and reveal hidden major factors from homogeneous parts. All member systems in an MSP share common governing principles of dynamics, but differ in idiosyncratic characteristics. A typical dynamic is found underlying response features with respect to covariate features of quantitative or qualitative data types. Neither all-system-as-one-whole nor individual system-specific functional structures are assumed in such response-vs-covariate (Re-Co) dynamics. We developed a computational protocol for identifying various collections of major factors of various orders underlying Re-Co dynamics. We first demonstrate the immanent effects of heterogeneity among member systems, which constrain compositions of major factors and even hide essential ones. Secondly, we show that fuller collections of major factors are discovered by breaking heterogeneity into many homogeneous parts. This process further realizes Anderson's "More is Different" phenomenon. We employ the categorical nature of all features and develop a Categorical Exploratory Data Analysis (CEDA)-based major factor selection protocol. Information theoretical measurements-conditional mutual information and entropy-are heavily used in two selection criteria: C1-confirmable and C2-irreplaceable. All conditional entropies are evaluated through contingency tables with algorithmically computed reliability against the finite sample phenomenon. We study one artificially designed MSP and then two real collectives of Major League Baseball (MLB) pitching dynamics with 62 slider pitchers and 199 fastball pitchers, respectively. Finally, our MSP data analyzing techniques are applied to resolve a scientific issue related to the Rosenberg Self-Esteem Scale.

Gender-Specific Impact of Metabolic Obesity Phenotypes on the Risk of Hashimoto's Thyroiditis: A Retrospective Data Analysis Using a Health Check-Up Database.

BACKGROUND: Hashimoto's thyroiditis (HT) is recognized as the most common autoimmune thyroid disease, often accompanied by the diffuse enlargement of thyroid with abundant blood flow and elevated level of thyroid autoantibodies. As obesity had a positive association with the risk of HT. Thus, this retrospective study was established to further explore the gender relationship between metabolic obesity phenotypes and the risk of Hashimoto's thyroiditis (HT). MATERIALS AND METHODS: Data for 3697 subjects aged ≥18 years were randomly collected from a Health check-up database from April to December 2019. Obesity was defined by general obesity (GO; body mass index [BMI] ≥28 kg/m2) and abdominal obesity (AO; waist circumstance, male ≥90 cm, female ≥85 cm). Metabolic unhealthy was defined as having at least one metabolic syndrome component and a homeostasis model assessment of insulin resistance ≥2.5. Obesity phenotypes were divided into three groups: GO, AO, compound obesity (GO+AO). After adjustment for potential confounding factors, multivariate logistic regression was used to assess the association between metabolic obesity phenotypes and risk of HT by sex and explore the correlation between different obesity patterns and HT risk by metabolic health status. RESULTS: The incidence of HT was 23.5% and significantly higher among females than males with different metabolic phenotypes (26.2% vs 20.5%, p<0.05), except metabolically healthy AO. Compared with non-obese subjects, different metabolic obesity phenotypes were independent risk factors among males (p<0.05). Among females, unhealthy metabolic status with GO (adjusted odds ratio [OR]=2.62) or AO (adjusted OR=2.87) and metabolically healthy non-GO (adjusted OR=2.05) were risk factors of HT (p<0.05). Increasing BMI categories and waist circumstance quartiles were positively correlated with HT risk (p for trend <0.05). Subgroup analyses indicated that GO+AO (adjusted OR=2.52) or only AO (adjusted OR=2.41) were risk factors for HT for those with unhealthy metabolic status. Moreover, GO+AO (adjusted OR=2.37) was an independent risk factor for HT under healthy metabolic status. CONCLUSION: GO+AO was associated with an increased risk of HT, identifying higher BMI/WC as a significant risk factor for HT. Males with unhealthy metabolic state or obesity and metabolically unhealthy females with obesity are high-risk group for HT. Additionally, only AO and GO+AO conferred increased risk of HT for individuals with metabolic abnormalities.

Clinical effect of methimazole combined with selenium in the treatment of toxic diffuse goiter in children.

BACKGROUND: The incidence of toxic diffuse goiter (Graves' disease) is higher in adolescents and preschool-aged children, with an upward trend. The incidence at 6-13 years of age is approximately 11.0%, and the incidences in men and women are 7.8% and 14.3%, respectively. AIM: To explore the clinical effect of methimazole combined with selenium in the treatment of toxic diffuse goiter (Graves' disease) in children and its effect on serum anti-thyroglobulin antibody (TRAb) and anti-thyroid peroxidase antibody (TPOAb). METHODS: A total of 103 children with Graves' disease treated in our hospital from January 2018 to June 2021 were divided into a traditional group and a combined group (15-20 mg methimazole orally given to children) and a combined group (50 µg selenium added on the basis of traditional treatment) according to different treatment methods to explore the therapeutic effects of the two methods and to observe the changes in thyroid volume and serum TRAb, TPOAb, free thyroxine (FT4) and inflammatory factor levels before and after treatment. The time taken for FT4 to return to normal was compared between the two groups. RESULTS: Treatment was significantly more effective in the combined group than in the traditional group (P < 0.05). The thyroid volumes of the children in the two groups was measured before and after treatment. Thyroid volume decreased significantly after treatment in both groups, and the thyroid volume was significantly lower in the combined group than in the traditional group (P < 0.05). The serum levels of interleukin-6 (IL-6), IL-8, TRAb, TPOAb and FT4 in the two groups were detected before and after treatment. The levels of IL-6, IL-8, TRAb, TPOAb and FT4 were significantly lower in the combined group than in the traditional group (P < 0.05). Follow-up of the children in the two groups showed that compared with the traditional group, it took less time for children in the combined group to return to the normal level (P < 0.05). CONCLUSION: Methimazole combined with selenium can effectively treat Graves' disease in children, reduce the expression of TRAb, TPOAb, FT4 and inflammatory factors, and improve the curative effect. Thus, the combined treatment warrants further clinical research.

Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics.

We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs.-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics' data analysis tasks by discovering major factors underlying such Re-Co dynamics by only making use of data's categorical nature. The major factor selection protocol at the heart of Categorical Exploratory Data Analysis (CEDA) paradigm is illustrated and carried out by employing Shannon's conditional entropy (CE) and mutual information (I[Re;Co]) as the two key Information Theoretical measurements. Through the process of evaluating these two entropy-based measurements and resolving statistical tasks, we acquire several computational guidelines for carrying out the major factor selection protocol in a do-and-learn fashion. Specifically, practical guidelines are established for evaluating CE and I[Re;Co] in accordance with the criterion called [C1:confirmable]. Following the [C1:confirmable] criterion, we make no attempts on acquiring consistent estimations of these theoretical information measurements. All evaluations are carried out on a contingency table platform, upon which the practical guidelines also provide ways of lessening the effects of the curse of dimensionality. We explicitly carry out six examples of Re-Co dynamics, within each of which, several widely extended scenarios are also explored and discussed.

Case report: A novel c.1842_1845dup mutation of ETFDH in two Chinese siblings with multiple acyl-CoA dehydrogenase deficiency.

This article reports the characterization of two siblings diagnosed with late-onset multiple Acyl-CoA dehydrogenase deficiency (MADD) caused by mutations in electron transfer flavoprotein(ETF)-ubiquinone oxidoreductase (ETF-QO) (ETFDH) gene. Whole exome sequencing (WES) was performed in the proband's pedigree. Clinical phenotypes of Proband 1 (acidosis, hypoglycemia, hypotonia, muscle weakness, vomiting, hypoglycemia, hepatomegaly, glutaric acidemia, and glutaric aciduria) were consistent with symptoms of MADD caused by the ETFDH mutation. However, Proband 2 presented with only a short stature. The patients (exhibiting Probands 1 and 2) showed identical elevations of C6, C8, C10, C12, and C14:1. c.1842_1845 (exon13)dup, and c.250 (exon3) G > A of the ETFDH gene were compound heterozygous variants in both patients. The novel variant c.1842_1845dup was rated as likely pathogenic according to the American College of Medical Genetics and Genomics guidelines (ACMG). This is the first report on the c.1842_1845dup mutation of the ETFDH gene in patients with late-onset MADD, and the data described herein may help expand the mutation spectrum of ETFDH.

Categorical Nature of Major Factor Selection via Information Theoretic Measurements.

Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener-Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member's major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.

Mimicking Complexity of Structured Data Matrix's Information Content: Categorical Exploratory Data Analysis.

We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features' categorical nature via histogram and it is guided by all features' associative patterns (order-2 dependence) in a mutual conditional entropy matrix. Higher-order structural dependency of k(≥3) features is exhibited through block patterns within heatmaps that are constructed by permuting contingency-kD-lattices of counts. By growing k, the resultant heatmap series contains global and large scales of structural dependency that constitute the data matrix's information content. When involving continuous features, the principal component analysis (PCA) extracts fine-scale information content from each block in the final heatmap. Our mimicking protocol coherently simulates this heatmap series by preserving global-to-fine scales structural dependency. Upon every step of mimicking process, each accepted simulated heatmap is subject to constraints with respect to all of the reliable observed categorical patterns. For reliability and robustness in sciences, CEDA with mimicking enhances data visualization by revealing deterministic and stochastic structures within each scale-specific structural dependency. For inferences in Machine Learning (ML) and Statistics, it clarifies, upon which scales, which covariate feature-groups have major-vs.-minor predictive powers on response features. For the social justice of Artificial Intelligence (AI) products, it checks whether a data matrix incompletely prescribes the targeted system.