Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.

BACKGROUND: Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature. OBJECTIVE: Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers' training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms. METHODS: Windowed timeline data of patients with a diagnosis of schizophrenia spectrum disorder before a known hospitalization event and healthy controls were gathered from 3 platforms: Facebook (254/268, 94.8% of participants), Twitter (51/268, 19% of participants), and Instagram (134/268, 50% of participants). We then used a 3 × 3 combinatorial binary classification design to train machine learning classifiers and evaluate their performance on testing data from all available platforms. We further compared results from models in intraplatform experiments (ie, training and testing data belonging to the same platform) to those from models in interplatform experiments (ie, training and testing data belonging to different platforms). Finally, we used Shapley Additive Explanation values to extract the top predictive features to explain and compare the underlying constructs that predict hospitalization on each platform. RESULTS: We found that models in intraplatform experiments on average achieved an F1-score of 0.72 (SD 0.07) in predicting a psychiatric hospitalization because of schizophrenia spectrum disorder, which is 68% higher than the average of models in interplatform experiments at an F1-score of 0.428 (SD 0.11). When investigating the key drivers for divergence in construct validities between models, an analysis of top features for the intraplatform models showed both low predictive feature overlap between the platforms and low pairwise rank correlation (<0.1) between the platforms' top feature rankings. Furthermore, low average cosine similarity of data between platforms within participants in comparison with the same measurement on data within platforms between participants points to evidence of identity fragmentation of participants between platforms. CONCLUSIONS: We demonstrated that models built on one platform's data to predict critical mental health treatment outcomes such as hospitalization do not generalize to another platform. In our case, this is because different social media platforms consistently reflect different segments of participants' identities. With the changing ecosystem of social media use among different demographic groups and as web-based identities continue to become fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required.

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

OBJECTIVES: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. METHODS: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. RESULTS: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33-0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07-0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23-2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20-76.70) or unverified (OR = 5.03; 95% CI, 1.66-15.24). CONCLUSIONS: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online "infodemics" to inform public health responses.

Using 'infodemics' to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July-September 2020.

BACKGROUND: Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic. METHODS: We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation. RESULTS: We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts' topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months. CONCLUSION: This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic.

Bioactive chemical constituents, in vitro anti-proliferative activity and in vivo toxicity of the extract of Curcuma singularis Gagnep rhizomes.

ETHNOPHARMACOLOGICAL RELEVANCE: Curcuma singularis Gagnep is a Vietnamese medicinal plant which has been commonly used as a medicinal remedy in traditional and folk medicines for improving health as well as for treating some diseases, like rheumatoid arthritis, kidney failure. However, pharmacological effects, including anti-cancer activity and the safety of this plant has not been fully investigated. AIM OF THE STUDY: This study aimed to investigate the in vitro anti-growth activity of an extract derived from Curcuma singularis rhizome extract (CSE) against cell lines as well as determine its phytochemical composition. The other goal of our study was to assess the safety of CSE in rats. MATERIALS AND METHODS: The main constituents in the extract were identified and quantitatively analyzed. The in vitro cytotoxicity of CSE was evaluated in several cancer and normal cell lines. The apoptotic activity of CSE and the expression of the apoptosis-related genes were investigated in AGS cells to clarify the underlying molecular mechanisms. The in vivo toxicity of CSE was assessed via acute and subacute oral studies on Sprague-Dawley rats, respectively according to the guidelines 425 and 407 of the Organization for Economic Cooperation and Development (OECD). The drug-related toxicity signs, mortality, body and organ weights were recoreded during the experimental period. In addition, the selected hematological and biochemical parameters, and histological alterations were determined at the end of the subacute toxicity test. RESULTS: Germacrone, ar-turmerone, and curcumol were three sesquiterpene components found in the extract. CSE showed cytotoxic effects in different cancer cells, but had minimal effects on normal cells. Apoptosis in AGS cells was caused by CSE in a concentration-dependent pattern through increase of Bax/Bcl-2 ratio, and release of cytochrome c, which leads to activation of caspase-3/-7, caspase-9, as well as cleavage of PARP. In the acute toxicity test, no signs of toxicity and no mortality were recorded in rats at both doses of 1000 and 5000 mg/kg. In the subacute toxicity study, CSE showed no drug-related adverse effects on water and food consumption, body and organ weights. CSE at a dose of 1000 mg/kg slightly increased WBC and platelet values in female rats, while it increased WBC values in male rats in all tested doses. The decrease of total cholesterol and triglyceride levels were found in female rats treated CSE at doses of 250 or 500 mg/kg. In addition, the increase of serum ALT and AST levels in rats treated at the dose of 1000 mg/kg were noted. No significant changes in histopathological structures of kidneys, spleen, heart and lungs, except liver tissue with minor modifications was found. CONCLUSIONS: Our findings indicated that CSE exhibited in vitro anti-proliferative effects on AGS cells by mainly activating the caspase-dependent mitochondrial apoptotic pathway. CSE also showed in vivo toxicity signals at the dose of 1000 mg/kg with proven minor hepatic injuries, which should be avoided the high dose for prolonged use. Curcuma singularis rhizomes may be used as a chemotherapeutic agent for the treatment of gastric cancer with in vitro anti-cancer investigation and in vivo biological safety evaluation.

Investigation of bioactive chemical constituents and anti-cancer activity of ethanol extract of Curcuma singularis Gagnep rhizomes.

Curcuma singularis Gagnep is a Vietnamese medicinal plant which has been commonly used in traditional and folk medicines for the treatment of different diseases. The goals of the present study are to investigate chemical composition and anti-proliferative activity of Curcuma singularis rhizome extract (CSE). The in vitro cytotoxicity of CSE was evaluated using WST-1 and LDH assays. The apoptosis induction was determined using nuclei DAPI staining and FACS assays. The main compounds of extract were identified and quantitatively analyzed using the validated HPLC method. The extract showed cytotoxic effects in various liver and breast cancer cells but had minimal effects on normal cells. It induced apoptosis on both Hep3B and SKBR3 cells in a dose-dependent manner. In addition, three sesquiterpene compounds, such as germacrone (3.25 ± 0.32 mg/g), ar-turmerone (1.12 ± 0.24 mg/g), and curcumol (0.31 ± 0.12 mg/g) were found as the main components of CSE. This is the first report on the in vitro cytotoxic effect of Curcuma singularis rhizomes against cancer cells.

Evidence-based recommender system for high-entropy alloys.

Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster-Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe-Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

Committee machine that votes for similarity between materials.

A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

Localized Charge Transfer in Two-Dimensional Molybdenum Trioxide.

Molybdenum trioxide is an interesting inorganic system in which the empty 4d states have potential to hold extra electrons and therefore can change states from insulating opaque (MoO3) to colored semimetallic (HxMoO3). Here, we characterize the local electrogeneration and charge transfer of the synthetic layered two-dimensional 2D MoO3-II (a polymorph of MoO3 and analogous to α-MoO3) in response to two different redox couples, i.e., [Ru(NH3)6]3+ and [Fe(CN)6]3- by scanning electrochemical microscopy (SECM). We identify the reduction of [Ru(NH3)6]3+ to [Ru(NH3)6]2+ at the microelectrode that leads to the reduction of MoO3-II to conducting blue-colored molybdenum bronze HxMoO3. It is recognized that the dominant conduction of the charges occurred preferentially at the edges active sites of the sheets, as edges of the sheets are found to be more conducting. This yields positive feedback current when approaching the microelectrode toward 2D MoO3-II-coated electrode. In contrast, the [Fe(CN)6]4-, which is reduced from [Fe(CN)6]3-, is found unfavorable to reduce MoO3-II due to its higher redox potential, thus showing a negative feedback current. The charge transfer on MoO3-II is further studied as a function of applied potential. The results shed light on the charge transfer behavior on the surface of MoO3-II coatings and opens the possibility of locally tuning of their oxidation states.

Direct Observation of Indium Conductive Filaments in Transparent, Flexible, and Transferable Resistive Switching Memory.

Electronics with multifunctionalities such as transparency, portability, and flexibility are anticipated for future circuitry development. Flexible memory is one of the indispensable elements in a hybrid electronic integrated circuit as the information storage device. Herein, we demonstrate a transparent, flexible, and transferable hexagonal boron nitride (hBN)-based resistive switching memory with indium tin oxide (ITO) and graphene electrodes on soft polydimethylsiloxane (PDMS) substrate. The ITO/hBN/graphene/PDMS memory device not only exhibits excellent performance in terms of optical transmittance (∼85% in the visible wavelength), ON/OFF ratio (∼480), retention time (∼5 × 104 s) but also shows robust flexibility under bending conditions and stable operation on arbitrary substrates. More importantly, direct observation of indium filaments in an ITO/hBN/graphene device is found via ex situ transmission electron microscopy, which provides critical insight on the complex resistive switching mechanisms.

Coexistence of Write Once Read Many Memory and Memristor in blend of Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate and Polyvinyl Alcohol.

In this work, the coexistence of Write Once Read Many Memory (WORM) and memristor can be achieved in a single device of Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT: PSS) and Polyvinyl Alcohol (PVA) blend organic memory system. In memristor mode, the bistable resistance states of the device can be cycled for more than 1000 times. Once a large negative bias of -8V was applied to the device, it was switched to permanent high resistance state that cannot be restored back to lower resistance states. The mechanism of the memristor effect can be attributed to the charge trapping behaviour in PVA while the WORM effect can be explained as the electrochemical characteristic of PEDOT: PSS which harnesses the percolative conduction pathways. The results may facilitate multipurpose memory device with active tunability.

Direct Observation of Conducting Filaments in Tungsten Oxide Based Transparent Resistive Switching Memory.

Transparent nonvolatile memory has great potential in integrated transparent electronics. Here, we present highly transparent resistive switching memory using stoichiometric WO3 film produced by cathodic electrodeposition with indium tin oxide electrodes. The memory device demonstrates good optical transmittance, excellent operative uniformity, low operating voltages (+0.25 V/-0.42 V), and long retention time (>104 s). Conductive atomic force microscopy, ex situ transmission electron microscopy, and X-ray photoelectron spectroscopy experiments directly confirm that the resistive switching effects occur due to the electric field-induced formation and annihilation of the tungsten-rich conductive channel between two electrodes. Information on the physical and chemical nature of conductive filaments offers insightful design strategies for resistive switching memories with excellent performances. Moreover, we demonstrate the promising applicability of the cathodic electrodeposition method for future resistive memory devices.

Resistive Switching Memory Phenomena in PEDOT PSS: Coexistence of Switchable Diode Effect and Write Once Read Many Memory.

We study resistive switching memory phenomena in conducting polymer PEDOT PSS. In the same film, there are two types of memory behavior coexisting; namely, the switchable diode effect and write once read many memory. This is the first report on switchable diode phenomenon based on conducting organic materials. The effect was explained as charge trapping of PEDOT PSS film and movement of proton. The same PEDOT PSS device also exhibits write once read many memory (WORM) phenomenon which arises due to redox reaction that reduces PEDOT PSS and renders it non-conducting. The revelation of these two types of memory phenomena in PEDOT PSS highlights the remarkable versatility of this conducting conjugated polymer.

Data mining for materials design: a computational study of single molecule magnet.

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn(4+)Mn3(3+) single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn(4+) and Mn(3+) ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

Program impact evaluation using a matching method with panel data.

Difference-in-differences with matching is a popular method to measure the impact of an intervention in health and social sciences. This method requires baseline data, that is, data before interventions, which are not always available in reality. Instead, panel data with two time periods are often collected after interventions begin. In this paper, a simple matching method is proposed to measure the impact of an intervention using two-period panel data after the intervention. The method is illustrated by the measurement of the effect of health insurance in Vietnam using household panel data.

Determination of thermodynamic parameters in reversed phase chromatography for polyethylene glycols and their methyl ethers in different mobile phases.

The thermodynamic parameters (entropy and enthalpy change) and their increment per repeat unit have been determined in RP chromatography of PEG and its mono- and dimethyl ethers in different mobile phases. The different approaches for their determination and the problems related to the characteristic volumes (void volume, interstitial volume, pore volume) of the column are discussed. Very different dependences or the thermodynamic parameters were observed in aqueous mobile phases containing ACN, acetone, or methanol as organic modifier. In the first two mobile phases a linear dependence on the number of repeat units n is found, and the additional entropy and enthalpy changes per repeat unit are positive. In methanol-water, the enthalpy change is negative with a non-linear dependence on n, and the entropy change is independent on n and close to zero. The contribution of the end groups is almost the same in the first two mobile phases, but much smaller in methanol-water.

Total transmission and total reflection by zero index metamaterials with defects.

We theoretically investigate microwave transmission through a zero-index metamaterial loaded with dielectric defects. The metamaterial is impedance matched to free space, with the permittivity and permeability tending towards zero over a given frequency range. By simply varying the radii and permittivities of the defects, total transmission or reflection of the impinging electromagnetic wave can be achieved. The proposed defect structure can offer advances in shielding or cloaking technologies without restricting the object's viewpoint. Active control of the observed exotic transmission and reflection signatures can occur by incorporating tunable refractive index materials such as liquid crystals and BaSrTiO3.

A data base for polymer chromatography: dependence of interaction parameters on mobile phase composition.

In chromatography of polymers, retention is determined by the characteristic volumes of the column (pore volume and interstitial volume), the pore diameter, and the interaction parameter. While the influence of the pore diameter is predominant in size exclusion chromatography, the key parameters in liquid adsorption chromatography are the interaction parameter and the pore surface of the column. It is shown, that the retention behaviour of polymers in liquid adsorption chromatography (LAC) can be predicted very well using the accessible volume and pore surface of the column, which can be determined very easily, and the interaction parameters from a data base.