Development of a dental diet-tracking mobile app for improved caries-related dietary behaviours: Key features and pilot evaluation of quality.

Objective: Diet significantly contributes to dental decay (caries) yet monitoring and modifying patients' diets is a challenge for many dental practitioners. While many oral health and diet-tracking mHealth apps are available, few focus on the dietary risk factors for caries. This study aims to present the development and key features of a dental-specific mobile app for diet monitoring and dietary behaviour change to prevent caries, and pilot data from initial user evaluation. Methods: A mobile app incorporating a novel photo recognition algorithm and a localised database of 208,718 images for food item identification was developed. The design and development process were iterative and incorporated several behaviour change techniques commonly used in mHealth. Pilot evaluation of app quality was assessed using the end-user version of the Mobile Application Rating Scale (uMARS). Results: User feedback from the beta-testing of the prototype app spurred the improvement of the photo recognition algorithm and addition of more user-centric features. Other key features of the final app include real-time prompts to drive actionable behaviour change, goal setting, comprehensive oral health education modules, and visual metrics for caries-related dietary factors (sugar intake, meal frequency, etc.). The final app scored an overall mean (standard deviation) of 3.6 (0.5) out of 5 on the uMARS scale. Conclusion: We developed a novel diet-tracking mobile app tailored for oral health, addressing a gap in the mHealth landscape. Pilot user evaluations indicated good app quality, suggesting its potential as a useful clinical tool for dentists and empowering patients for self-monitoring and behavioural management.

Crystal Structure and Bond-Valence Investigation of Nitrogen-Stabilized LiAl5O8 Spinels.

An in-depth insight into the effect of nitrogen substitution on structural stabilization is important for the design of new spinel-type oxynitride materials with tailored properties. In this work, the crystal structures of ordered and disordered LiAl5O8 obtained by slow cooling and rapid quenching, respectively, were analyzed by a X-ray diffraction (XRD) Rietveld refinement and OccQP program. The variation in the bonding state of atoms in the two compounds was explored by the bond valence model, which revealed that the instability of spinel-type LiAl5O8 crystal structure at room temperature is mainly due to the severe under-bonding of the tetrahedrally coordinated Al cations. With the partial substitution of oxygen with nitrogen in LiAl5O8, a series of the nitrogen-stabilized spinel LiyAl(16+x-y)/3O8-xNx (0 < x < 0.5, 0 < y < 1) was successfully prepared. The crystal structures were systematically investigated by the powder XRD structural refinement combined with 7Li and 27Al magic-angle spinning nuclear magnetic resonance. All the Li+ ions entered the octahedra, while the Al resonances may be composed of multiple non-equivalent Al sites. The structural stability of spinel LiyAl(16+x-y)/3O8-xNx at ambient temperature was attributed to the cationic vacancies and high valence generated by the N ions, which alleviated the under-bonding state of the tetrahedral Al-O bond. This work provides a new perspective for understanding the composition-structure relationship in spinel compounds with multiple disorders.

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review.

Background: There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area. Methods: This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool. Results: A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing. Conclusions: While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study.

BACKGROUND: Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. OBJECTIVE: The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. METHODS: The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter's corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. RESULTS: The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. CONCLUSIONS: We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

Interaction Between Amorolfine and Voriconazole Against Fusarium species.

Fusarium species represent a range of fungal pathogens capable of causing diverse mycotic diseases. Relative to antibacterial drugs, few effective antifungal agents have been developed to date, and all are subject to significant limitations. As such, there is an urgent need to design novel antifungal treatments for infections caused by Fusarium spp. Herein, 15 clinical isolates, including 5 Fusarium oxysporum and 10 Fusarium solani strains, were analyzed to explore the relative inhibitory effects of different combinations of amorolfine (AMO) and voriconazole (VOR) on the growth of these fungal pathogens. These analyses were conducted by measuring minimal inhibitory concentration (MIC) values for these antifungal agents in a broth microdilution assay and by using an in vivo model of Fusarium-infected Galleria mellonella. These experiments revealed that in isolation, AMO and VOR exhibited MIC values ranging from 4 to 16 µg/mL and 2 to 8 µg/mL, respectively. However, these effective MIC values fell to 1-2 µg/mL and 0.5-2 µg/mL, respectively, when AMO and VOR were administered in combination with one another, exhibiting synergistic activity against 73.3% of analyzed Fusarium strains. Subsequent in vivo analyses conducted using the G. mellonella model further confirmed that combination VOR + AMO treatment was associated with significantly improved larval survival following Fusarium spp. infection. Together, these results serve as the first published evidence demonstrating that VOR and AMO exhibit synergistic activity against infections caused by Fusarium spp., indicating that they may represent an effective approach to antifungal disease treatment.

Particulate matter 2.5 regulates lipid synthesis and inflammatory cytokine production in human SZ95 sebocytes.

A large body of evidence indicates that particulate matter (PM)2.5 is associated with various negative effects on human health. However, the impact and molecular mechanism of PM2.5 on the skin have not been elucidated. Therefore, the present study aimed to investigate the effects of two types of PM2.5 [water-soluble extracts (W-PM2.5) and non-water-soluble extracts (NW-PM2.5)] on cell proliferation, cell cycle progression, lipid synthesis, and inflammatory cytokine production of human SZ95 sebocytes. The results demonstrated that NW-PM2.5 and W-PM2.5 exposure dose-dependently inhibited SZ95 sebocyte proliferation by inducing G1 cell arrest. Furthermore, NW-PM2.5 and W-PM2.5 significantly reduced sebaceous lipid synthesis and markedly promoted the production of inflammatory cytokines, including interleukin-1α (IL-1α), IL-6 and IL-8 in SZ95 sebocytes. Additionally, the expression of aryl hydrocarbon (Ah) receptor (AhR), AhR nuclear translocator protein (ARNT), as well as cytochrome P450 1A1 were significantly increased following PM2.5 exposure. Thus, these findings indicate that PM2.5 exerts inhibitory effects on cell proliferation and lipid synthesis, and stimulatory effects on inflammatory cytokine production and AhR signaling activation in human SZ95 sebocytes.

A novel truncating mutation of PTCH1 in a Chinese family with Gorlin syndrome.

Gorlin syndrome, a rare autosomal dominant disease, is characterized by numerous basal cell carcinomas, multiple jaw cysts, palmar and plantar pits and embryological deformities. Mutations in the PTCH1 gene are the most common molecular defects associated with Gorlin syndrome. We detected a duplication of thymine after nucleotide position 2927 in exon 18 of the PTCH1 gene (c.2927 dupT) in a fifty-year-old male proband with peri-anal basal cell carcinoma and his brother. The mutation creates a frameshift and leads to a premature stop codon (p.Tyr977 Leufs* 16) lacking 5 of the 12 transmembrane-spanning domains. However, the functional significance of truncation of the N terminal regions remains currently unknown and to be further investigated. The current findings indicate that genetic testing of PTCH1 gene mutational status may aid in the early diagnosis of Gorlin syndrome in which multiple complex abnormalities are present, hampering prompt diagnosis and treatment.