Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

[This corrects the article DOI: 10.2196/46036.].

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

Bulk Mn2+ Doped 1D Hybrid Lead Halide Perovskite with Highly Efficient, Tunable and Stable Broadband Light Emissions.

Mn2+ doped colloidal three-dimensional (3D) lead halide perovskite nanocrystal (PNC) has attracted intensive research attention; however, the low exciton binding energy and fatal optical instability of 3D PNC seriously hinder the optoelectronic application. Therefore, it remains significant to explore new stable host perovskite with strongly bound exciton to realize more desirable luminescent property. In this work, we utilized bulk one-dimensional (1D) hybrid perovskite of [AEP]PbBr5 ⋅ H2 O (AEP=N-aminoethylpiperazine) as structural platform to rationally optimize the luminescent property by a controllable Mn2+ doping strategy. Significantly, the series of Mn2+ -doped 1D [AEP]PbBr5 ⋅ H2 O show enhanced energy transfer efficiency from the strongly bound excitons of host material to 3d electrons of Mn2+ ions, resulting in tunable broadband light emissions from weak yellow to strong red spectral range with highest photoluminescence quantum yield up to 28.41 %. More importantly, these Mn2+ -doped 1D perovskites display ultrahigh structural and optical stabilities in humid atmosphere, water and high temperature exceeding the conventional 3D PNC. Combined highly efficient, tunable and stable broadband light emissions enable Mn2+ -doped 1D perovskite as excellent down-converting phosphor showcasing the potential application in white light emitting diode. This work not only provides a profound understanding of low-dimensional perovskites but also opens a new way to rationally design high-performance broadband light emitting perovskites for solid-state lighting and displaying devices.

Personal motivation, self-regulation barriers and strategies for weight loss in people with overweight and obesity: a thematic framework analysis.

OBJECTIVE: To explore motivations, self-regulation barriers and strategies in a multi-ethnic Southeast Asian population with overweight and obesity. DESIGN: Qualitative design using semi-structured face-to-face and videoconferencing interviews. Data were analysed using thematic framework analysis and constant comparison method. SETTING: Specialist weight management clinic. PARTICIPANTS: Twenty-two participants were purposively sampled from 13 April to 30 April 2021. Median age and BMI of the participants were 37·5 (interquartile range (IQR) = 13·3) and 39·2 kg/m2 (IQR = 6·1), respectively. And 31·8 % were men, majority had a high intention to adopt healthy eating behaviours (median = 6·5; IQR = 4·8-6·3) and 59 % of the participants had a medium level of self-regulation. RESULTS: Six themes and fifteen subthemes were derived. Participants were motivated to lose weight by the sense of responsibility as the family's pillar of support and to feel 'normal' again. We coupled self-regulation barriers with corresponding strategies to come up with four broad themes: habitual overconsumption - mindful self-discipline; proximity and convenience of food available - mental tenacity; momentary lack of motivation and sense of control - motivational boosters; and overeating triggers - removing triggers. We highlighted six unique overeating triggers namely: trigger activities (e.g. using social media); eating with family, friends and colleagues; provision of food by someone; emotions (e.g. feeling bored at home, sad and stressed); physiological condition (e.g. premenstrual syndrome); and the time of the day. CONCLUSIONS: Future weight management interventions should consider encompassing participant-led weight loss planning, motivation boosters and self-regulation skills to cope with momentary overeating triggers.

Uses of a dedicated 3D reconstruction software with augmented and mixed reality in planning and performing advanced liver surgery and living donor liver transplantation (with videos).

The development of digital intelligent diagnostic and treatment technology has opened countless new opportunities for liver surgery from the era of digital anatomy to a new era of digital diagnostics, virtual surgery simulation and using the created scenarios in real-time surgery using mixed reality. In this article, we described our experience on developing a dedicated 3 dimensional visualization and reconstruction software for surgeons to be used in advanced liver surgery and living donor liver transplantation. Furthermore, we shared the recent developments in the field by explaining the outreach of the software from virtual reality to augmented reality and mixed reality.

Cerebral blood flow alterations in migraine patients with and without aura: An arterial spin labeling study.

BACKGROUND: Migraine aura is a transient, fully reversible visual, sensory, or other central nervous system symptom that classically precedes migraine headache. This study aimed to investigate cerebral blood flow (CBF) alterations of migraine with aura patients (MwA) and without aura patients (MwoA) during inter-ictal periods, using arterial spin labeling (ASL). METHODS: We evaluated 88 migraine patients (32 MwA) and 44 healthy control subjects (HC) who underwent a three-dimensional pseudo-continuous ASL MRI scanning. Voxel-based comparison of normalized CBF was conducted between MwA and MwoA. The relationship between CBF variation and clinical scale assessment was further analyzed. The mean CBF values in brain regions showed significant differences were calculated and considered as imaging features. Based on these features, different machine learning-based models were established to differentiate MwA and MwoA under five-fold cross validation. The predictive ability of the optimal model was further tested in an independent sample of 30 migraine patients (10 MwA). RESULTS: In comparison to MwoA and HC, MwA exhibited higher CBF levels in the bilateral superior frontal gyrus, bilateral postcentral gyrus and cerebellum, and lower CBF levels in the bilateral middle frontal gyrus, thalamus and medioventral occipital cortex (all p values < 0.05). These variations were also significantly correlated with multiple clinical rating scales about headache severity, quality of life and emotion. On basis of these CBF features, the accuracies and areas under curve of the final model in the training and testing samples were 84.3% and 0.872, 83.3% and 0.860 in discriminating patients with and without aura, respectively. CONCLUSION: In this study, CBF abnormalities of MwA were identified in multiple brain regions, which might help better understand migraine-stroke connection mechanisms and may guide patient-specific decision-making.

A Zero-Dimensional Hybrid Cadmium Perovskite with Highly Efficient Orange-Red Light Emission.

Low-dimensional organic-inorganic hybrid metal halide materials have been extensively studied due to their excellent optoelectronic performances. Herein, by using the facile wet-chemistry method, we designed one new hybrid cadmium bromide of (H3AEP)2CdBr6·2Br based on discrete octahedral [CdBr6]4- units. Remarkably, the bulk crystal of (H3AEP)2CdBr6·2Br exhibits strong broadband orange-red light emission from the radiative recombination of self-trapped excitons (STEs) with a high photoluminescence quantum yield (PLQY) of 9%. Benefiting from the highly efficient luminescent performance, this 0D cadmium perovskite can be utilized as an excellent down-conversion red phosphor to assemble a white light-emitting diode, and a high color rendering index (CRI) of 93 is realized. As far as we know, this is the first orange-red light-emitting hybrid cadmium perovskite which promotes the full-color display in this system.

Simultaneous removal of fluoride, manganese and iron by manganese oxide supported activated alumina: characterization and optimization via response surface methodology.

Fluoride, iron and manganese simultaneous exceedance of standard can be observed in groundwater in northeastern China. This work aims to apply a highly efficient method combining adsorption and oxidation for the synchronous removal of the inorganic ions. An innovative adsorbent (manganese-supported activated alumina) was synthesized by the impregnation method and showed a significant adsorption capacity better than that of fresh activated alumina. The characterization (scanning electron microscope; Brunauer, Emmett and Teller; X-ray diffraction and Fourier transform infrared spectroscopy) results verified the successful introduction of MnOOH and MnO2, and the improvement of surface microstructure enhanced the removal ability. The effect of single factors, such as pH value, reaction time or dosage on the removal performance has been verified. The maximum removal efficiencies of fluoride, iron and manganese were optimized via Response surface methodology considering the independent factors in the range of MO@AA dosage (5-9 g/L), pH (4-6) and contact time (4-12 h). Noted that compared with control, MO@AA exhibited 59.4% of improved fluoride performance. At pH of 5.79, contacting time of 12 h and 8.21 g/L of MO@AA, fluoride, iron and manganese removal were found to be 91, 100 and 23%, respectively. Herein, MO@AA was distinguished as good applicability for the treatment of fluoride-, iron- and manganese-containing groundwater.

Clinical management and outcomes of acute pancreatitis: Identifying areas for quality improvement in a tertiary Asian setting.

BACKGROUND: This study aims to review the clinical management of patients with acute pancreatitis in a tertiary institute in Singapore, and to identify areas qualiy improvement based on validation against the recommendations in the IAP/APA and the Japanese guidelines. METHODS: 391 patients from a prospective electronic database were included and reviewed for compliance to the International Association of Pancreatology (IAP)/American Pancreatic Association (APA) guidelines (2013) and the Japanase Guidelines (2015). RESULTS: The 90 day mortality was 8.4% for moderately severe and 11.9% for severe pancreatitis. The accuracy of SIRS in predicting severe acute pancreatitis on admission was 72.1% and at 48 h 80.8%. Only 61.1% patients had ultrasound scan during their admission of whom 32.9% had it within 24 h of admission. 18.3% patients with initial diagnosis of idiopathic pancreatitis had EUS. 50% received Ringer lactate for initial fluid resuscitation. 38.7% received antibiotics as prophylaxis. 21.4% with severe acute pancreatitis had early enteral nutrition. Only 21.4% patients with biliary pancreatitis had index admission cholecystectomy. CONCLUSION: The compliance to existing guidelines for management of acute pancreatitis is variable. Identifying gaps and implementing measures to address them allows for continued improvement in the management of patients with acute pancreatitis.

Reduced formation of brominated trihalomethanes during chlorination of bromide-containing waters in the presence of Mn(II).

Manganese(II) (Mn(II)) and bromide (Br-) are common in natural waters. This study investigated the effect of in-situ Mn(II) oxidation and preformed MnOx on the brominated trihalomethane (Br-THM) formation during chlorination of bromide-containing waters. The results showed Br-THM formation could be substantially inhibited by in-situ Mn(II) oxidation, but the addition of preformed MnOx had limited influence on Br-THM formation during chlorination of bromide-containing waters. Analysis of bromine species showed that about 30 % bromine species were incorporated into the MnOx particles and formed MnOx-Br during the in-situ Mn(II) oxidation process. Consequently, the availability of reactive bromine species for the reaction with dissolved organic matter (DOM) reduced, leading to less Br-THM formation. X-ray diffraction (XRD) analysis of in-situ Mn(II) oxidation product indicated the presence of Br- decreased the crystallinity of Mn oxides, verifying the bromine species entered MnOx crystal. However, the adsorptive uptake of bromine species by preformed MnOx was negligible and had no impact on Br-THM formation. Inhibition rate of Mn(II) oxidation on THM formation decreased with increasing specific ultraviolet absorbance (SUVA254) value of filtered water, showing SUVA254 could be a good indicator of DOM competition ability for oxidant with Mn(II). In addition, Excitation/Emission Matrix indicated that Mn(II) could form complexes with humic substances, which might also retard the reaction between humic substances and oxidant to form Br-THMs. This study highlighted the inhibiting effect of in-situ Mn(II) oxidation on Br-THM formation during chlorination of bromide-containing waters.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops. At the same time, named entity recognition still faces many challenges. In this paper, we propose an improved bidirectional gated recurrent unit neural network (BI-GRU) method, which is used to automatically identify the required entities (i.e. gene names, rice phenotypes) from relevant rice literature and patents. The neural network model is combined with the Softmax function to directly output the probabilities of labels, forming the BI-GRU-SF model. With the ability of deep learning methods, the semantic information in the context can be learned without the need for feature engineering. Finally, we conducted experiments, and the results showed that our proposed model provided better performance compared to other models. All datasets and resource codes of BI-GRU-SF are available at https://github.com/qqeeqq/NER for academic use.

Synthesis of acylated derivatives of alginate oligosaccharides and evaluation of their potential antibacterial activities against Staphylococcus aureus.

Alginate oligosaccharides (AOS) are crucial carbohydrate-based biomaterial used in the synthesis of potential drugs and biological agents, but their antibacterial activities are not significant. In this study, AOS acylated derivatives were synthesized by grafting maleic anhydride (MA) onto AOS at varying ratios. Additionally, their inhibitory effects against Staphylococcus aureus were thoroughly investigated. Characterization of the AOS acylated derivatives (AOS-MA-x, where x = 1, 5, 10, and 20) was conducted using Fourier-transformed infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, and X-ray diffraction, which confirmed the successful synthesis of these derivatives. The bacteriostatic activity of the AOS-MA derivatives was assessed using growth curves and plate coating method, demonstrating significant antibacterial effects against S. aureus, as compared with AOS. Among these derivatives, AOS-MA-20 exhibited the most potent bacteriostatic activity and was selected for further investigation of its inhibitory mechanism. Scanning electron microscopy analysis revealed that treatment with AOS-MA-20 led to the lysis and rupture of S. aureus cells, expelling their intracellular contents. Moreover, AOS-MA-20 disrupted the integrity of cell wall and cell membrane, impacted ATPase activity, and inhibited the formation of biofilm to some extent, ultimately resulting in bacterial death. These findings lay a foundational framework for the development of environmentally friendly antimicrobial agents.

Brain connectome-based imaging markers for identifiable signature of migraine with and without aura.

Background: Cortical spreading depression (CSD) has been considered the prominent theory for migraine with aura (MwA). However, it is also argued that CSD can exist in patients in a silent state, and not manifest as aura. Thus, the MwA classification based on aura may be questionable. This study aimed to capture whole-brain connectome-based imaging markers with identifiable signatures for MwA and migraine without aura (MwoA). Methods: A total of 88 migraine patients (32 MwA) and 49 healthy controls (HC) underwent a diffusion tensor imaging and resting-state functional magnetic resonance imaging scan. The whole-brain structural connectivity (SC) and functional connectivity (FC) analysis was employed to extract imaging features. The extracted features were subjected to an all-relevant feature selection process within cross-validation loops to pinpoint attributes demonstrating substantial efficacy for patient categorization. Based on the identified features, the predictive ability of the random forest classifiers constructed with the 88 migraine patients' sample was tested using an independent sample of 32 migraine patients (eight MwA). Results: Compared to MwoA and HC, MwA showed two reduced SC and six FC (five increased and one reduced) features [all P<0.01, after false discovery rate (FDR) correction], involving frontal areas, temporal areas, visual areas, amygdala, and thalamus. A total of four imaging features were significantly correlated with clinical rating scales in all patients (r=-0.38 to 0.47, P<0.01, after FDR correction). The predictive ability of the random forest classifiers achieved an accuracy of 78.1% in the external sample to identify MwA. Conclusions: The whole-brain connectivity features in our results may serve as connectome-based imaging markers for MwA identification. The alterations of SC and FC strength provide possible evidence in further understanding the heterogeneity and mechanism of MwA which may help for patient-specific decision-making.

Aberrant brain functional hubs and causal effective connectivity in menstrually-related and non-menstrually-related migraine without aura.

Background: Menstrual migraine without aura (MRM) is common in female migraineurs and is closely related to cerebral functional abnormalities. However, whether the whole brain networks and directional functional connectivity of MRM patients are altered remains unclear. The purpose of this study was to detect the alterations of resting-state functional networks and directional functional connectivity between MRM and non-menstrual migraine without aura (NMM) patients using functional magnetic resonance imaging (fMRI) with degree centrality (DC) and Granger causality analysis (GCA) methods. Methods: In this retrospective and cross-sectional study, 45 MRM and 40 NMM patients (matched in age, gender, and years of education) were recruited in the study between May 2018 and June 2022. All participants had undergone resting-state fMRI scanning at the Neurology and Pain Outpatient Department of Nanjing First Hospital. Their brain functions were analyzed in terms of DC and GCA, with the significant threshold at voxel level P<0.01 and cluster level P<0.05, Gaussian random field corrected. Correlation analysis was adopted to assess the relationships between the fMRI results and clinical features (P<0.05, Bonferroni corrected). Results: Compared with those in the NMM group, MRM patients showed decreased DC in the right insula (T=-4.253). Using the right insula as the seed region, patients with MRM demonstrated enhanced effective connectivity from the right insula to the ipsilateral middle temporal gyrus (T=4.138) and contralateral superior temporal gyrus (T=3.523). Furthermore, the MRM group also showed decreased effective connectivity from several brain regions to the right insula, which included the right inferior occipital gyrus (T=-4.498), left middle frontal gyrus (T=-4.879), right precuneus (T=-4.644), and left inferior parietal gyrus (T=-4.113). The average Self-rating Anxiety Scale score of the MRM group was significantly higher than that of the NMM group [P=0.032, 95% confidence interval (CI): 0.363-7.761]. In the MRM group, disease duration was negatively correlated with the mean value of DC in right insula (r=-0.428, P=0.01). Conclusions: The present research demonstrated that patients with MRM have disruption in insula resting-state functional networks. Disrupted networks contained regions associated with cognitive processes, emotional perception, and migraine attack in MRM patients. These results may improve our comprehension of the neuromechanism of menstrually-related migraine.

Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study.

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2-28.4 kg/m2) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity. Conclusion: UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.

Iron particles lower than 10 µm in drinking water dominate particle catalysis effect on disinfection byproduct formation.

Iron particles could catalyze disinfection by-product (DBP) formation in drinking water distribution systems (DWDS), but the catalytic effects of iron particles considering size effects have not been focused. Here, we first found that fine particles (lower than 10 µm) dominated the particle catalysis effect of the iron particles on the formation of DBPs containing multiple Cl atoms (DBP-3Cl), especially those with aromatic structure and containing multiple N atoms (DBP-3N). The loose deposit particles were filtered through 50 µm (F50), 10 µm (F10) and 1 µm (F10) membranes, and their turbidity values were 231.6, 53.4 and 1.1 NTU, respectively. In mass ratio, F50, F10 and F1 accounted for 84 %, 15 % and 1 % of unfiltered samples. Notably, the lower mass F10 generated more DBP-3Cl and DBP-3N than F50. Metal crystals and natural organic matters showed little difference among different sizes. The high catalytic activity of particles in F10 due to size effect was proved to be the essential mechanism. F1 contained few particles to affect DBP formation. In toxicity evaluation, the toxicity of F10 was even higher than F50. Therefore, fine particles with sizes lower than 10 µm may play a dominate role in the catalytic effect on DBP transformation in DWDS.

Use of Mixed Reality in Neuro-Oncology: A Single Centre Experience.

(1) Background: Intra-operative neuronavigation is currently an essential component to most neurosurgical operations. Recent progress in mixed reality (MR) technology has attempted to overcome the disadvantages of the neuronavigation systems. We present our experience using the HoloLens 2 in neuro-oncology for both intra- and extra-axial tumours. (2) Results: We describe our experience with three patients who underwent tumour resection. We evaluated surgeon experience, accuracy of superimposed 3D image in tumour localisation with standard neuronavigation both pre- and intra-operatively. Surgeon training and usage for HoloLens 2 was short and easy. The process of image overlay was relatively straightforward for the three cases. Registration in prone position with a conventional neuronavigation system is often difficult, which was easily overcome during use of HoloLens 2. (3) Conclusion: Although certain limitations were identified, the authors feel that this system is a feasible alternative device for intra-operative visualization of neurosurgical pathology. Further studies are being planned to assess its accuracy and suitability across various surgical disciplines.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis.

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.