The effectiveness of virtual passport, an app-based intervention, for dementia care.

Background and objectives: This study aimed to confirm the effectiveness of the virtual passport for dementia care. Research design and methods: The virtual passport is an application (app) tool connecting healthcare professionals, dementia care sites, and people living with dementia (PLWD) and their family caregivers. This tool assists case managers in hospitals by providing individualized care plans and health education to PLWD and their caregivers. The dementia quality indicator achievement rates, care needs investigation and fulfillment, severity of behavioral and psychological symptoms of dementia (BPSD), and changes in caregiver burden and depression are measured at the initial interview and 6 and 12 months after the intervention. Results: We enrolled 57 and 54 patients and their caregivers in the virtual passport and routine care groups, respectively. Compared to the control group, six quality indicators in the passport group showed significantly higher achievement at 6 months after using the virtual passport. Case managers addressed more care needs at 6 months (1.37 vs 0, p < 0.001) and 12 months (1.32 vs 0, p < 0.001). Improvement in severity of neuropsychiatric symptoms (neuropsychiatric inventory (NPI) irritability/lability difference: -0.58 vs 0.22, p = 0.044; NPI agitation/aggression difference =-0.78 vs 0.00, p = 0.042) were also observed. No obvious influence was found in caregiver burden and depression after using the virtual passport. Discussion and implications: The virtual passport is an effective information technology tool in improving the quality of dementia care, assisting case management in identifying more care needs, and reducing the severity of BPSD.

Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

BACKGROUND: International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). OBJECTIVE: This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). METHODS: By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. RESULTS: Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. CONCLUSIONS: An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

Recent advance on lignin-containing nanocelluloses: The key role of lignin.

Nanocelluloses (NCs) isolated from lignocellulosic resources usually require harsh chemical pretreatments to remove lignin, which face constraints such as high energy consumption and inefficient resource utilization. An alternative strategy involving the partial retention of lignin can be adopted to endow NCs with better versatility and functionality. The resulting lignin-containing nanocelluloses (LNCs) generally possess better mechanical property, thermal stability, barrier property, antioxidant activity, and surface hydrophobicity than lignin-free NCs, which have attracted extensive interest as a promising green nanomaterial for numerous applications. This review provides a comprehensive overview of the recent advances in the preparation, properties, and food application of LNCs. The effect of residual lignin on the preparation and properties of LNCs is discussed. Furthermore, the key roles of lignin in the properties of LNCs, including particle size, crystalline structure, dispersibility, thermal, mechanical, antibacterial, rheological and adhesion properties, are summarized comprehensively. Furthermore, capitalizing on their dietary fiber and nanostructure properties, the food applications of LNCs in the forms of films, gels and emulsions are also discussed. Finally, the challenges and opportunities regarding the development of LNCs are provided.

A human autoimmune organoid model reveals IL-7 function in coeliac disease.

In vitro models of autoimmunity are constrained by an inability to culture affected epithelium alongside the complex tissue-resident immune microenvironment. Coeliac disease (CeD) is an autoimmune disease in which dietary gluten-derived peptides bind to the major histocompatibility complex (MHC) class II human leukocyte antigen molecules (HLA)-DQ2 or HLA-DQ8 to initiate immune-mediated duodenal mucosal injury1-4. Here, we generated air-liquid interface (ALI) duodenal organoids from intact fragments of endoscopic biopsies that preserve epithelium alongside native mesenchyme and tissue-resident immune cells as a unit without requiring reconstitution. The immune diversity of ALI organoids spanned T cells, B and plasma cells, natural killer (NK) cells and myeloid cells, with extensive T-cell and B-cell receptor repertoires. HLA-DQ2.5-restricted gluten peptides selectively instigated epithelial destruction in HLA-DQ2.5-expressing organoids derived from CeD patients, and this was antagonized by blocking MHC-II or NKG2C/D. Gluten epitopes stimulated a CeD organoid immune network response in lymphoid and myeloid subsets alongside anti-transglutaminase 2 (TG2) autoantibody production. Functional studies in CeD organoids revealed that interleukin-7 (IL-7) is a gluten-inducible pathogenic modulator that regulates CD8+ T-cell NKG2C/D expression and is necessary and sufficient for epithelial destruction. Furthermore, endogenous IL-7 was markedly upregulated in patient biopsies from active CeD compared with remission disease from gluten-free diets, predominantly in lamina propria mesenchyme. By preserving the epithelium alongside diverse immune populations, this human in vitro CeD model recapitulates gluten-dependent pathology, enables mechanistic investigation and establishes a proof of principle for the organoid modelling of autoimmunity.

Gelatin-nanocellulose stabilized emulsion-filled hydrogel beads loaded with curcumin: Preparation, encapsulation and release behavior.

In this study, the curcumin was firstly encapsulated in gelatin (GLT) and/or cellulose nanocrystals (CNC) stabilized emulsions, then further mixed with sodium alginate (SA) to form emulsion-filled hydrogel beads loaded with curcumin (Cur). The Cur-loaded emulsions showed a droplet size of 20.3-24.4 µm with a uniform distribution. Introducing CNC and/or SA increased the viscosity of emulsions accompanied by viscoelastic transition, while the modulus was reduced due to destruction of GLT gel. Cur was doubly immobilized in the hydrogel beads with >90 % of encapsulation efficiency. The results of simulated gastrointestinal tract experiments revealed that the beads possessed a good pH sensitivity and controlled release behavior to prolong the retention of Cur in the gastrointestinal tract. After 6 h of UV irradiation, the Cur-loaded emulsion-filled hydrogel beads showed a higher antioxidant activity than that of pure Cur, effectively delaying the photodegradation of Cur. In addition, the beads had better stability in aqueous and acidic environments, which was favorable for prolonging the release of Cur. These results suggest that the emulsion-filled hydrogel beads have great potential for the delivery of lipophilic bioactive molecules.

Recent advances in nanocellulose based hydrogels: Preparation strategy, typical properties and food application.

Nanocellulose has been favored as one of the most promising sustainable nanomaterials, due to its competitive advantages and superior performances such as hydrophilicity, renewability, biodegradability, biocompatibility, tunable surface features, excellent mechanical strength, and high specific surface area. Based on the above properties of nanocellulose and the advantages of hydrogels such as high water absorption, adsorption, porosity and structural adjustability, nanocellulose based hydrogels integrating the benefits of both have attracted extensive attention as promising materials in various fields. In this review, the main fabrication strategies of nanocellulose based hydrogels are initially discussed in terms of different crosslinking methods. Then, the typical properties of nanocellulose based hydrogels are comprehensively summarized, including porous structure, swelling ability, adsorption, mechanical, self-healing, smart response performances. Especially, relying on these properties, the general application of nanocellulose based hydrogels in food field is also discussed, mainly including food packaging, food detection, nutrient embedding delivery, 3D food printing, and enzyme immobilization. Finally, the safety of nanocellulose based hydrogel is summarized, and the current challenges and future perspectives of nanocellulose based hydrogels are put forward.

Properties regulation and mechanism on ferritin/chitooligosaccharide dual-compartmental emulsions and its application for co-encapsulation of curcumin and quercetin bioactive compounds.

Dual-compartmental emulsions, containing multiple chambers, possess great advantages in co-encapsulation of different cargoes. Herein, we reported a stable dual-compartmental emulsion by regulating the ratio of Marsupenaeus japonicus ferritin (MF) and chitooligosaccharide (COS), enabling efficient co-encapsulation of different compounds. The adsorption behavior of MF/COS complex over droplet interface varied at different ratios, thereby exerting an influence on the emulsion properties. Remarkably, emulsions stabilized by MF/COS complex at a ratio of 2:1 exhibited superior stability, as evidenced by no significant creaming or demulsification during storage or heat treatment. The mechanism is that MF/COS2:1 complex can enhance the formation of thicker interfacial layer and dense continuous phase network structure. Additionally, curcumin and quercetin can be co-encapsulated into the emulsions and their retention rates were significantly improved than those in oils, implying the potential of the resulting dual-compartmental emulsions in co-encapsulation and delivery of bioactive compounds.

Low gelatin concentration assisted cellulose nanocrystals stabilized high internal phase emulsion: The key role of interaction.

Low concentrations of gelatin (0.02-0.20 wt%) were applied to regulate the surface and interface properties of CNC (0.50 wt%) by forming CNC/G complexes. As gelatin concentration increased from 0 to 0.20 wt%, the potential value of CNC/G gradually changed from -44.50 to -17.93 mV. Additionally, various gelatin concentrations led to micromorphology changes of CNC/G complexes, with the formation of particle interconnection at gelatin concentration of 0.10 wt%, followed by network structure and enhanced aggregation at gelatin concentration of 0.15 and 0.20 wt% respectively. The water contact angle (25.91°-80.23°) and interface adsorption capacity of CNC/G were improved due to hydrophobic group exposure of gelatin. When gelatin concentration exceeded 0.10 % at a fixed oil phase volume fraction (75 %), a high internal phase emulsion (HIPE) stabilized by CNC/G can be formed with a good storage stability. The rheological and microstructure results of HIPE confirmed that low gelatin concentration can assist CNC to form stable emulsion structure. Especially, the auxiliary stabilization mechanism of various gelatin concentration was different. CNC/G-0.10 % and CNC/G-0.15 % stabilized HIPE mainly depended on the enhanced interface adsorption and network structure, while CNC/G-0.20 % stabilized HIPE mainly relied on enhanced interface adsorption/accumulation due to weak electrostatic repulsion and aggregate granular morphology of CNC/G-0.20 %.

Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Preparation of nanocellulose light porous material adsorbed with tannic acid and its application in fresh-keeping pad.

This study fabricated nanocellulose lightweight porous material (TOCNF-G-LPM-TA) as absorbent fresh-keeping pad for meat products, using TEMPO-oxidized cellulose nanofibril (TOCNF) and gelatin as structural skeleton and tannic acid (TA) as antibacterial component of TOCNF lightweight porous material (TOCNF-G-LPM). The adsorption kinetics, capacity and mechanism of TOCNF-G-LPM in different initial concentrations of TA solutions were investigated, the antioxidant and antibacterial properties of TOCNF-G-LPM-TA and its fresh-keeping effect on refrigerated pork at 4 ℃ were studied. Due to strong hydrogen bonding and porous structure, TOCNF-G-LPM exhibited excellent TA adsorption ability (230 mg/g) conforming with pseudo-second-order kinetic and Langmuir isotherm models. TA endowed TOCNF-G-LPM with good antioxidant and antibacterial activities. According to changes in appearance, pH and TVB-N values of pork during storage at 4 ℃, TOCNF-G-LPM-TA effectively extended the shelf life of refrigerated pork. This work provides a facile method for preparing nanocellulose based absorbent fresh-keeping pads.

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.

BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. OBJECTIVE: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. METHODS: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models' outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. RESULTS: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model's performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. CONCLUSIONS: The study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.

Effect of electrostatic repulsion on barrier properties and thermal performance of gelatin films by carboxymethyl starch, and application in food cooking.

Carboxymethyl starch (CST) was introduced to improve gelatin films and its practical application as edible high-performance films for food packaging and cooking was also investigated. The gelatin films modified by carboxymethyl starch exhibited the transparent appearance, tensile strength, barrier properties (oxygen, water vapor and UV light), and thermal performance (TGA, thermal shrinkage and heat-sealing strength). Resulting from the effect of electrostatic interaction modes on the properties of films, electrostatic repulsion could surpass electrostatic attraction in improving the tensile strength, oxygen barrier property and thermal stability of the films probably due to extensive physical entanglement without aggregation. Analysis of FTIR, zeta potential, interfacial dilatational rheology, shear rheological properties, XRD, Raman, SEM and AFM suggested that hydrogen bonding and electrostatic repulsion contributed to the excellent performance. The packaged food could also be cooked with the prepared film for porridge; and the film slightly influenced the shear rheological properties of porridge and imposed little effect on the odors (Electronic-Nose) of porridge. Hence, the gelatin films modified by carboxymethyl starch could potentially work as the edible inner packaging or the edible quantitative packaging for food, offer convenience for consumers, reduce the packaging waste and avoid an extra burden on environment.

Intratumor injected gold molecular clusters for NIR-II imaging and cancer therapy.

Surgical resections of solid tumors guided by visual inspection of tumor margins have been performed for over a century to treat cancer. Near-infrared (NIR) fluorescence labeling/imaging of tumor in the NIR-I (800 to 900 nm) range with systemically administrated fluorophore/tumor-targeting antibody conjugates have been introduced to improve tumor margin delineation, tumor removal accuracy, and patient survival. Here, we show Au25 molecular clusters functionalized with phosphorylcholine ligands (AuPC, ~2 nm in size) as a preclinical intratumorally injectable agent for NIR-II/SWIR (1,000 to 3,000 nm) fluorescence imaging-guided tumor resection. The AuPC clusters were found to be uniformly distributed in the 4T1 murine breast cancer tumor upon intratumor (i.t.) injection. The phosphocholine coating afforded highly stealth clusters, allowing a high percentage of AuPC to fill the tumor interstitial fluid space homogeneously. Intra-operative surgical navigation guided by imaging of the NIR-II fluorescence of AuPC allowed for complete and non-excessive tumor resection. The AuPC in tumors were also employed as a photothermal therapy (PTT) agent to uniformly heat up and eradicate tumors. Further, we performed in vivo NIR-IIb (1,500 to 1,700 nm) molecular imaging of the treated tumor using a quantum dot-Annexin V (QD-P3-Anx V) conjugate, revealing cancer cell apoptosis following PTT. The therapeutic functionalities of AuPC clusters combined with rapid renal excretion, high biocompatibility, and safety make them promising for clinical translation.

Collagen and gelatin: Structure, properties, and applications in food industry.

Food-producing animals have the highest concentration of collagen in their extracellular matrix. Collagen and gelatin are widely used in food industry due to their specific structural, physicochemical, and biochemical properties, which enable them to improve health and nutritional value as well as to increase the stability, consistency, and elasticity of food products. This paper reviews the structural and functional properties including inherent self-assembly, gel forming, water-retaining, emulsifying, foaming, and thickening properties of collagen and gelatin. Then the colloid structures formed by collagen such as emulsions, films or coatings, and fibers are summarized. Finally, the potential applications of collagen and gelatin in muscle foods, dairy products, confectionary and dessert, and beverage products are also reviewed. The objective of this review is to provide the current market value, progress as well as applications of collagen and its derivatives in food industry.

Rechargeable Li/Cl2 Battery Down to -80 °C.

Low temperature rechargeable batteries are important to life in cold climates, polar/deep-sea expeditions, and space explorations. Here, this work reports 3.5-4 V rechargeable lithium/chlorine (Li/Cl2 ) batteries operating down to -80 °C, employing Li metal negative electrode, a novel carbon dioxide (CO2 ) activated porous carbon (KJCO2 ) as the positive electrode, and a high ionic conductivity (≈5-20 mS cm-1 from -80 °C to room-temperature) electrolyte comprised of aluminum chloride (AlCl3 ), lithium chloride (LiCl), and lithium bis(fluorosulfonyl)imide (LiFSI) in low-melting-point (-104.5 °C) thionyl chloride (SOCl2 ). Between room-temperature and -80 °C, the Li/Cl2 battery delivers up to ≈29 100-4500 mAh g-1 first discharge capacity (based on carbon mass) and a 1200-5000 mAh g-1 reversible capacity over up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron spectroscopy probe Cl2 trapped in the porous carbon upon LiCl electro-oxidation during charging. At -80 °C, Cl2 /SCl2 /S2 Cl2 generated by electro-oxidation in the charging step are trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ≈4 V with up to ≈1000 mAh g-1 capacity for SCl2 /S2 Cl2 reduction and up to ≈4000 mAh g-1 capacity at ≈3.1 V plateau for Cl2 reduction.

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study.

BACKGROUND: Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning-based methods have been shown to be effective in deidentification. However, very few studies investigated the combination of transformer-based language models and rules. OBJECTIVE: The objective of this study is to develop a hybrid deidentification pipeline for Australian EHR text notes using rules and transformers. The study also aims to investigate the impact of pretrained word embedding and transformer-based language models. METHODS: In this study, we present a hybrid deidentification pipeline called OpenDeID, which is developed using an Australian multicenter EHR-based corpus called OpenDeID Corpus. The OpenDeID corpus consists of 2100 pathology reports with 38,414 SHI entities from 1833 patients. The OpenDeID pipeline incorporates a hybrid approach of associative rules, supervised deep learning, and pretrained language models. RESULTS: The OpenDeID achieved a best F1-score of 0.9659 by fine-tuning the Discharge Summary BioBERT model and incorporating various preprocessing and postprocessing rules. The OpenDeID pipeline has been deployed at a large tertiary teaching hospital and has processed over 8000 unstructured EHR text notes in real time. CONCLUSIONS: The OpenDeID pipeline is a hybrid deidentification pipeline to deidentify SHI entities in unstructured EHR text notes. The pipeline has been evaluated on a large multicenter corpus. External validation will be undertaken as part of our future work to evaluate the effectiveness of the OpenDeID pipeline.

Mechanism on the Synergistic Gelation of the Myofibrillar Protein Composite Gel Enhanced by "Clean-Label" Skin Functional Protein Powders.

The concept of healthiness and sustainability has promoted the innovation and development of "clean-label" products. Herein, this study aims to explore the influence mechanism of "clean label" skin protein powder (FPP) on the gelation properties of myofibrillar proteins (MPs). Specifically, the addition of FPP (0.2-4.0%) can improve the water holding capability and texture properties of MP composite gels. When the FPP concentration is over 1.0%, the composite gels exhibit no significant water loss during centrifugation. Dynamic rheology and sodium-dodecyl sulfate-polyacrylamide gel electrophoresis results revealed that FPP can slow the aggregation and denaturation of myosin and promote the formation of disulfide bonds between myofibril proteins, thus forming a stable network structure. Structural observation revealed that FPP can fill into the MP gel and lead to the formation of compact gel structures. Besides, with the increase of FPP concentration, the chemical forces involved in structural stabilization change significantly. Specifically, hydrophobic interaction and hydrogen bonding are the dominant forces at a lower FPP concentration (0.2 and 0.4%), while the ionic bond and disulfide bond are the dominant forces at a higher concentration. Overall, this work demonstrated that FPP can significantly improve the gel functionality of MP by altering the gel structure and strengthening the molecular forces.

Shedding light on rechargeable Na/Cl2 battery.

Advancing new ideas of rechargeable batteries represents an important path to meeting the ever-increasing energy storage needs. Recently, we showed rechargeable sodium/chlorine (Na/Cl2) (or lithium/chlorine Li/Cl2) batteries that used a Na (or Li) metal negative electrode, a microporous amorphous carbon nanosphere (aCNS) positive electrode, and an electrolyte containing dissolved aluminum chloride and fluoride additives in thionyl chloride [G. Zhu et al., Nature 596, 525-530 (2021) and G. Zhu et al., J. Am. Chem. Soc. 144, 22505-22513 (2022)]. The main battery redox reaction involved conversion between NaCl and Cl2 trapped in the carbon positive electrode, delivering a cyclable capacity of up to 1,200 mAh g-1 (based on positive electrode mass) at a ~3.5 V discharge voltage [G. Zhu et al., Nature 596, 525-530 (2021) and G. Zhu et al., J. Am. Chem. Soc. 144, 22505-22513 (2022)]. Here, we identified by X-ray photoelectron spectroscopy (XPS) that upon charging a Na/Cl2 battery, chlorination of carbon in the positive electrode occurred to form carbon-chlorine (C-Cl) accompanied by molecular Cl2 infiltrating the porous aCNS, consistent with Cl2 probed by mass spectrometry. Synchrotron X-ray diffraction observed the development of graphitic ordering in the initially amorphous aCNS under battery charging when the carbon matrix was oxidized/chlorinated and infiltrated with Cl2. The C-Cl, Cl2 species and graphitic ordering were reversible upon discharge, accompanied by NaCl formation. The results revealed redox conversion between NaCl and Cl2, reversible graphitic ordering/amorphourization of carbon through battery charge/discharge, and probed trapped Cl2 in porous carbon by XPS.

Multiplexed discrimination of SARS-CoV-2 variants via plasmonic-enhanced fluorescence in a portable and automated device.

Portable assays for the rapid identification of lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to aid large-scale efforts in monitoring the evolution of the virus. Here we report a multiplexed assay in a microarray format for the detection, via isothermal amplification and plasmonic-gold-enhanced near-infrared fluorescence, of variants of SARS-CoV-2. The assay, which has single-nucleotide specificity for variant discrimination, single-RNA-copy sensitivity and does not require RNA extraction, discriminated 12 lineages of SARS-CoV-2 (in three mutational hotspots of the Spike protein) and detected the virus in nasopharyngeal swabs from 1,034 individuals at 98.8% sensitivity and 100% specificity, with 97.6% concordance with genome sequencing in variant discrimination. We also report a compact, portable and fully automated device integrating the entire swab-to-result workflow and amenable to the point-of-care detection of SARS-CoV-2 variants. Portable, rapid, accurate and multiplexed assays for the detection of SARS-CoV-2 variants and lineages may facilitate variant-surveillance efforts.

Stabilization of capsanthin in physically-connected hydrogels: Rheology property, self-recovering performance and syringe/screw-3D printing.

This work presented a facile way of stabilizing capsanthin by physically-connected soft hydrogels via utilizing specially-structured polysaccharides, and investigated rheological properties, self-recovering mechanism and 3D printability. The functionalized hydrogels demonstrated excellent color quality including redness, yellowness index and hue with great storage stability and visual perception. The soft hydrogels fabricated with properly sequenced polyglyceryl fatty acid esters, ß-cyclodextrin, chitosan, and low-content capsanthin possessed outstanding extrudability, appropriate yield stress, reasonable mechanical strength, rational elasticity and structure sustainability. Furthermore, the self-recovering properties based on hydrogen bonds, host-guest interactions and electrostatic interactions were revealed and verified by structural, zeta potential, micro-morphological, zeta potential, thixotropic, creep-recovery, and macroscopic/microscopic characterizations. Along with excellent antioxidant performance, the subsequent 3D printing onto bread with complex models elucidated the high geometry accuracy and great sensory characters. The sequenced physically-connected hydrogels incorporated with capsanthin can provide new insights on stabilizing hydrophobic biomaterials and developing the 3D printed exquisite, innovative food.