Tag: nevin manimala

ACS Appl Mater Interfaces. 2024 Aug 23. doi: 10.1021/acsami.4c07960. Online ahead of print.

ABSTRACT

Purpose/Aim: Acrylamides are hydrolytically stable at pH lower than 2, and were shown to preserve bonded interface integrity with two-step, total etch adhesives. The objective of this study was to leverage those two characteristics in self-etching primers containing the acidic monomer 10-MDP and test the microtensile bond strength before and after incubation with S. mutans incubation. Materials and Methods: Acidic primers (10 wt % 10-methacryloyloxydecyl dihydrogen phosphate─10-MDP; 45 wt % N,N-diethyl-1,3-bis(acrylamido)propane─DEBAAP, or 2-hydroxyethyl methacrylate─HEMA; 45 wt %, glycerol-dimethacrylate─GDMA) and adhesives (DEBAAP or HEMA/10-MDP/UDMA 45/10/45 wt %) were made polymerizable by the addition of 0.2 wt % camphorquinone, 0.8 wt % ethyl-4-dimethylaminobenzoate, 0.4 wt % diphenyliodonium hexafluorophosphate, and 0.1 wt % butylhydroxytoluene. Nonsolvated materials were characterized for flexural strength (FS), modulus (E), toughness, water sorption/solubility (WS/SL), contact angle, and vinyl conversion (DC). Viscosity was evaluated after adding 20 and 40 vol % ethanol to the primer and adhesive, respectively. The experimental materials or Clearfil SE Bond (CC─commercial control) were used to bond a commercial composite (Filtek Supreme) to the flat surface of human dentin. Microtensile bond strength (MTBS) was tested in 1 mm² sticks for the 5 primer/bond combinations: CC (Clearfil Bond Primer and Bond), HH (HEMA/HEMA), DD (DEBAAP/DEBAAP), HD (HEMA/DEBAAP), and DH (DEBAAP/HEMA). Prior to testing, sticks were stored in water or biofilm-inducing culture medium with S. mutans for 1 week. Confocal images and FTIR-ATR evaluation evaluated the hybrid layer of the adhesives. Results were analyzed using Student’s t-test (WS, SL, DC, contact angle, FS, E, toughness), one-way ANOVA/Tukey’s test for viscosity, and two-way ANOVA/Tukey’s test for MTBS (95%). Results: HEMA-based materials had lower contact angle (p = 0.004), higher WS (p < 0.001), and similar SL values compared to DEBAAP (p = 0.126). FS (p = 0.171) and E (p = 0.065) dry values were similar, but after one week of water storage, FS/E dropped more significantly for HEMA materials. Dry and wet toughness was greater for DEBAAP (p < 0.001), but it also had the greatest drop (46%). Clearfil bonds had the highest viscosity, followed by DEBAAP and HEMA, respectively (p = 0.002). For the primers, HEMA had the lowest viscosity (p = 0.003). As far as MTBS, all groups tested in water were statistically different when compared with HH (p < 0.001). After storage in biofilm, DH had the highest MTBS value, being statistically different from HH (p = 0.002), CC (p = 0.015), and DD (p = 0.027). Conclusions: The addition of a diacrylamide and its association with HEMA in self-etching adhesive systems provided greater bonding stability after bacterial challenge.

PMID:39178414 | DOI:10.1021/acsami.4c07960

J Med Internet Res. 2024 Aug 23;26:e54616. doi: 10.2196/54616.

ABSTRACT

BACKGROUND: For medical diagnosis, clinicians typically begin with a patient’s chief concerns, followed by questions about symptoms and medical history, physical examinations, and requests for necessary auxiliary examinations to gather comprehensive medical information. This complex medical investigation process has yet to be modeled by existing artificial intelligence (AI) methodologies.

OBJECTIVE: The aim of this study was to develop an AI-driven medical inquiry assistant for clinical diagnosis that provides inquiry recommendations by simulating clinicians’ medical investigating logic via reinforcement learning.

METHODS: We compiled multicenter, deidentified outpatient electronic health records from 76 hospitals in Shenzhen, China, spanning the period from July to November 2021. These records consisted of both unstructured textual information and structured laboratory test results. We first performed feature extraction and standardization using natural language processing techniques and then used a reinforcement learning actor-critic framework to explore the rational and effective inquiry logic. To align the inquiry process with actual clinical practice, we segmented the inquiry into 4 stages: inquiring about symptoms and medical history, conducting physical examinations, requesting auxiliary examinations, and terminating the inquiry with a diagnosis. External validation was conducted to validate the inquiry logic of the AI model.

RESULTS: This study focused on 2 retrospective inquiry-and-diagnosis tasks in the emergency and pediatrics departments. The emergency departments provided records of 339,020 consultations including mainly children (median age 5.2, IQR 2.6-26.1 years) with various types of upper respiratory tract infections (250,638/339,020, 73.93%). The pediatrics department provided records of 561,659 consultations, mainly of children (median age 3.8, IQR 2.0-5.7 years) with various types of upper respiratory tract infections (498,408/561,659, 88.73%). When conducting its own inquiries in both scenarios, the AI model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curve of 0.955 (95% CI 0.953-0.956) and 0.943 (95% CI 0.941-0.944), respectively. When the AI model was used in a simulated collaboration with physicians, it notably reduced the average number of physicians’ inquiries to 46% (6.037/13.26; 95% CI 6.009-6.064) and 43% (6.245/14.364; 95% CI 6.225-6.269) while achieving areas under the receiver operating characteristic curve of 0.972 (95% CI 0.970-0.973) and 0.968 (95% CI 0.967-0.969) in the scenarios. External validation revealed a normalized Kendall τ distance of 0.323 (95% CI 0.301-0.346), indicating the inquiry consistency of the AI model with physicians.

CONCLUSIONS: This retrospective analysis of predominantly respiratory pediatric presentations in emergency and pediatrics departments demonstrated that an AI-driven diagnostic assistant had high diagnostic performance both in stand-alone use and in simulated collaboration with clinicians. Its investigation process was found to be consistent with the clinicians’ medical investigation logic. These findings highlight the diagnostic assistant’s promise in assisting the decision-making processes of health care professionals.

PMID:39178403 | DOI:10.2196/54616

J Craniofac Surg. 2024 Aug 26. doi: 10.1097/SCS.0000000000010473. Online ahead of print.

ABSTRACT

This paper presents the findings of an observational study involving 38 patients to evaluate the application of a surgical technique utilizing an autologous costal cartilage scaffold for correcting nasal tip and alar asymmetry in unilateral cleft lip-nasal deformity. Nasal septum extension spreader grafts (SEG) and warped alar batten grafts, both made from autologous costal cartilage, were utilized in open rhinoplasty procedures. The warped alar batten graft was fixed to the caudal end of the SEG, with the lower lateral cartilage on the cleft side suspended to the free part of the newly created warped alar batten graft to lift the collapsed nasal alar further. Measurements of nasal tip height, nostril height, and the intersection angle of the nasal sill and alar (α) were taken before and after surgery, comparing the ratios between the normal and cleft sides. Patients were followed up for 2.5 to 5.5 years, with all cases showing successful healing and no complications. Postoperative improvements in nasal tip and nostril asymmetries were significant, with statistically significant changes observed in nasal tip height, nostril height, and the intersection angle of nasal sill and alar (α) (P<0.05). The combined use of SEG and warped alar batten graft, both crafted from autologous costal cartilage, effectively corrected nasal tip and alar asymmetry in adult cleft lip nasal deformity cases.

PMID:39178388 | DOI:10.1097/SCS.0000000000010473

JCO Clin Cancer Inform. 2024 Aug;8:e2400052. doi: 10.1200/CCI.24.00052.

ABSTRACT

PURPOSE: The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.

METHODS: We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older.

RESULTS: Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality.

CONCLUSION: This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

PMID:39178364 | DOI:10.1200/CCI.24.00052

Acad Med. 2024 Aug 23. doi: 10.1097/ACM.0000000000005855. Online ahead of print.

ABSTRACT

PURPOSE: Accelerated 3-year programs (A3YPs) at medical schools were developed to address student debt and mitigate workforce shortage issues. This study investigated whether medical school length (3 vs 4 years) was associated with early residency performance. The primary research question was as follows: Are the Accreditation Council for Graduate Medical Education Milestones (MS) attained by A3YP graduates comparable to graduates of traditional 4-year programs (T4YPs) at 6 and 12 months into internship?

METHOD: The MS data from students entering U.S. medical schools in 2021 and 2022 from the 6 largest specialties were used: emergency medicine, family medicine, internal medicine, general surgery, psychiatry, and pediatrics. Three-year and 4-year graduates were matched for analysis (2,899 matched learners: 182 in A3YPs and 2,717 in T4YPs). The study used a noninferiority study design to examine data trends between the study cohort (A3YP) and control cohort (T4YP). To account for medical school and residency program effects, the authors used cross-classified random-effects regression to account for clustering and estimate group differences.

RESULTS: The mean Harmonized MS ratings for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .77). Mean MS ratings across internal medicine MS for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = -0.03 [0.03], P = .31). Similarly, for family medicine, there were no statistically significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .96).

CONCLUSIONS: For the specialties studied, there were no significant differences in MS performance between 3-year and 4-year graduates at 6 and 12 months into internship. These results support comparable efficacy of A3YPs in preparing medical students for residency.

PMID:39178363 | DOI:10.1097/ACM.0000000000005855

Hum Reprod Update. 2024 Aug 23:dmae026. doi: 10.1093/humupd/dmae026. Online ahead of print.

ABSTRACT

BACKGROUND: Intermittent fasting, such as during Ramadan, is prevalent among pregnant women. However, the association between Ramadan during pregnancy and offspring health along the life course has not been fully established.

OBJECTIVE AND RATIONALE: Fetal programming research indicates that prenatal exposures, particularly during early pregnancy, can cause long-term structural and physiological changes that adversely affect offspring health. Our objective was to systematically identify and assess the evidence regarding Ramadan during pregnancy.

SEARCH METHODS: A total of 31 studies were sourced from PubMed, EMBASE, Web of Science, and EconLit. Included studies evaluated outcomes in individuals with prenatal Ramadan exposure, compared to unexposed Muslim controls. Main outcomes were birth weight, gestational length, and sex ratio in newborns; height, mortality, and cognition in children; and disabilities, chronic diseases, and human capital accumulation in adults. Each study was evaluated for risk of bias. The overall quality of evidence was appraised using the GRADE system. Random-effects meta-analyses were conducted for outcomes analyzed in at least three primary studies.

OUTCOMES: The initial search identified 2933 articles, 1208 duplicates were deleted. There were 31 publications fulfilled the eligibility criteria for the qualitative synthesis; 22 studies were included in meta-analyses. The overall quality of the evidence was low to moderate and differed by study design and outcome. Among newborns, prenatal Ramadan exposure was not associated with birth weight (mean difference (MD) -3 g (95% CI -18 to 11; I2 = 70%) or the likelihood of prematurity (percentage point difference (PPD) 0.19 (95% CI -0.11 to 0.49; I2 = 0%)). The probability that the newborn is male was reduced (PPD -0.14 (95% CI -0.28 to -0.00; I2 = 0%)). This potentially reflects sex-specific mortality rates resulting from adverse in utero circumstances. In childhood, the exposed performed slightly poorer on cognitive tests (MD -3.10% of a standard deviation (95% CI -4.61 to -1.58; I2 = 51%)). Height among the exposed was reduced, and this pattern was already visible at ages below 5 years (height-for-age z-score MD -0.03 (95% CI -0.06 to -0.00; I2 = 76%)). A qualitative literature synthesis revealed that childhood mortality rates were increased in low-income contexts. In adulthood, the prenatally exposed had an increased likelihood of hearing disabilities (odds ratio 1.26 (95% CI 1.09 to 1.45; I2 = 32%)), while sight was not affected. Other impaired outcomes included chronic diseases or their symptoms, and indicators of human capital accumulation such as home ownership (qualitative literature synthesis). The first trimester emerged as a sensitive period for long-term impacts.

WIDER IMPLICATIONS: Despite the need for more high-quality studies to improve the certainty of the evidence, the synthesis of existing research demonstrates that Ramadan during pregnancy is associated with adverse offspring health effects in childhood and especially adulthood, despite an absence of observable effects at birth. Not all health effects may apply to all Muslim communities, which are diverse in backgrounds and behaviors. Notably, moderating factors like daytime activity levels and dietary habits outside fasting hours have hardly been considered. It is imperative for future research to address these aspects.

REGISTRATION NUMBER: PROSPERO (CRD42022325770).

PMID:39178355 | DOI:10.1093/humupd/dmae026

PLoS One. 2024 Aug 23;19(8):e0307381. doi: 10.1371/journal.pone.0307381. eCollection 2024.

ABSTRACT

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term “dynamic” frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques.

PMID:39178296 | DOI:10.1371/journal.pone.0307381

PLoS One. 2024 Aug 23;19(8):e0307117. doi: 10.1371/journal.pone.0307117. eCollection 2024.

ABSTRACT

BACKGROUND: We sought to conduct this comprehensive systematic review and meta-analysis to assess the prevalence of depression, anxiety, and sleep disturbance in Iranian medical students and resident physicians.

METHODS: A systematic search was conducted on 23 December 2023 in PubMed/MEDLINE, Web of Science, Scopus, and Iranian national databases. We pooled the prevalence of individual studies using the random effect model.

RESULTS: Our systematic search showed 36 articles that meet the eligibility criteria. Most included studies were cross-sectional. The most used questionnaire to assess depression, anxiety, and sleep disturbance were Beck Depression Inventory (BDI), The Depression, Anxiety and Stress Scale-21 Items (DASS-21), and The Pittsburgh Sleep Quality Index (PSQI), respectively. The overall prevalence of depression, anxiety, and sleep disturbance among Iranian medical students were 43% (95%CI: 33%-53%%, I2 = 98%), 44% (95%CI: 31%-58%%, I2 = 99%), 48% (95%CI: 39%-56%%, I2 = 97%), respectively. The results of subgroup and meta-regression analyses showed questionnaires used and the place of the medical school were significantly associated with the prevalence of aforementioned outcomes. Funnel plot and Begg’s regression test did not show a significant source of funnel plot asymmetry for depression, anxiety, and sleep disturbance.

CONCLUSION: In conclusion, our study showed that nearly half of the medical students had some type of depression, anxiety, and sleep disturbance problems. To address this serious national public health issue, efficient preventive measures, routine screenings, and prompt interventions are required.

PMID:39178292 | DOI:10.1371/journal.pone.0307117

PLoS One. 2024 Aug 23;19(8):e0309175. doi: 10.1371/journal.pone.0309175. eCollection 2024.

ABSTRACT

AIM: In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.

METHODS: We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.

RESULTS: We screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.

CONCLUSION: This review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.

PMID:39178283 | DOI:10.1371/journal.pone.0309175

PLoS One. 2024 Aug 23;19(8):e0309442. doi: 10.1371/journal.pone.0309442. eCollection 2024.

ABSTRACT

The livestock sector contributes almost 11% of Pakistan’s GDP and is crucial to 35 million people’s livelihoods. Ticks are a major economic threat, as over 80% of livestock, such as bovines, are tick-infested with Hyalomma and Rhipicephalus tick species. Hyalomma anatolicum and Rhipicephalus microplus are the most common tick species collected from livestock, transmitting primarily anaplasmosis, babesiosis, and theileriosis. We aimed to identify the geographical distribution of these two tick species and hot spot areas where the risk of these diseases being transmitted by these ticks is high. Following the PRISMA guideline, two authors conducted an independent review of literature sourced from various databases. We screened 326 research articles published between January 1, 1990, and December 31, 2023, focused on identifying the tick species at the district level. Thirty studies from 75 districts, representing 49.3% of the country’s total area, detected at least one tick species through collection from animals. R. microplus was present in 81% (n = 61) and H. anatolicum in 82% (n = 62) of these sampled districts. We employed spatial and conventional statistical methods with Geographic Information Systems (GIS) after mapping the weighted distribution of both ticks (the number of ticks per standard unit of sampling effort). We identified northwestern and northcentral regions of the country as hotspots with the highest tick distribution, which aligned with the documented high prevalence of anaplasmosis, babesiosis, Crimean-Congo hemorrhagic fever (CCHF), and theileriosis in these regions. This underscores the urgent need for robust tick control measures in these districts to safeguard animal health and boost the livestock economy.

PMID:39178282 | DOI:10.1371/journal.pone.0309442