Functional Training for Lower Extremities in Stroke Survivors: A Scoping Review.

Engaging in meaningful and repetitive goal-oriented functional tasks can effectively enhance neuroplasticity and facilitate recovery following a stroke. This particular approach has primarily been studied in relation to functional outcomes and has predominantly focused on late subacute and chronic stroke patients. However, there is a lack of information regarding the standardized protocol of lower extremity functional training, its constituent elements, and its impact on motor recovery during the early subacute phase of stroke. The aim of this study was to examine the available evidence related to the intervention protocol of lower extremity functional training in order to identify common training elements and assess their impact on motor and functional outcomes in stroke survivors. A systematic search was conducted on PubMed and Scopus, covering the period from 2000 to 2022. A total of 1786 articles were retrieved and screened based on predefined inclusion criteria. A total of 36 articles were included in this review. The primary findings were classified into categories such as intervention protocols for functional training and their constituent elements, outcome measures utilized, minimal clinically important differences (MCID) reported, and the conclusions drawn by the respective studies. Only a limited quantity of studies reported on the intervention protocol of lower extremity functional training. The majority of these studies focused on the efficacy of functional training for enhancing gait and balance, as evaluated through functional outcome assessments, particularly in the context of chronic stroke patients. In most studies, the evaluation of outcomes was typically based on statistical significance rather than clinical significance. In light of these findings, it is recommended that future studies be conducted during the early subacute phase of stroke to further investigate the impact of functional training on motor outcomes. This will contribute to a broader understanding of the benefits of functional training in facilitating motor recovery in the lower extremities and its clinical significance in stroke survivors.

Deep learning approaches for breast cancer detection in histopathology images: A review.

BACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. OBJECTIVE: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. METHODS: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. RESULTS: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. CONCLUSION: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Computer-Aided detection of tuberculosis from X-ray images using CNN and PatternNet classifier.

BACKGROUND: Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease. Computer-aided techniques can be used to assist radiologists in interpreting X-ray images, which can improve the ease and accuracy of diagnosis. OBJECTIVE: To develop a computer-aided technique for the diagnosis of TB from X-ray images using deep learning techniques. METHODS: This research paper presents a novel approach for TB diagnosis from X-ray using deep learning methods. The proposed method uses an ensemble of two pre-trained neural networks, namely EfficientnetB0 and Densenet201, for feature extraction. The features extracted using two CNNs are expected to generate more accurate and representative features than a single CNN. A custom-built artificial neural network (ANN) called PatternNet with two hidden layers is utilized to classify the extracted features. RESULTS: The effectiveness of the proposed method was assessed on two publicly accessible datasets, namely the Montgomery and Shenzhen datasets. The Montgomery dataset comprises 138 X-ray images, while the Shenzhen dataset has 662 X-ray images. The method was further evaluated after combining both datasets. The method performed exceptionally well on all three datasets, achieving high Area Under the Curve (AUC) scores of 0.9978, 0.9836, and 0.9914, respectively, using a 10-fold cross-validation technique. CONCLUSION: The experiments performed in this study prove the effectiveness of features extracted using EfficientnetB0 and Densenet201 in combination with PatternNet classifier in the diagnosis of tuberculosis from X-ray images.

A comparison of physiotherapy students' perception about blended learning with online learning during COVID-19 pandemic: A mixed method of study.

BACKGROUND: The COVID-19 pandemic has affected face-to-face teaching across the globe. The sudden shift in learning methods has impacted learning experiences significantly. Students' perception about online compared to blended learning might affect learning. The objective of this study was to evaluate physiotherapy students' perception of blended compared to online learning. MATERIALS AND METHODS: This mixed-method study documents physiotherapy students' perception about the courses delivered through blended learning (BL) mode during the COVID-19 pandemic. Physiotherapy graduates and postgraduate students who completed their evidence-based physiotherapy practice courses at Sri Ramachandra Institute of Higher Education and Research, Chennai (N = 68) participated in this study. The participants' perceived experience about synchronous online mode and BL during the pandemic was assessed using a questionnaire and focus group discussion. RESULTS: All the participants felt that the course outcomes were met and that they gained knowledge and skills in evidence-based practice. Most of the students (93%) recommended a blended mode of learning compared to online learning alone. Thematic analysis of the focus group discussion (FGD) identified enhanced learning experience, collaborative learning as enablers to BL, and availability of gadgets and quality of online contents as barriers. CONCLUSION: Participants showed par preference for blended learning over online learning as it provided flexibility and facilitated active learning compared to online learning alone.

Delivery Strategies for mRNA Vaccines.

The therapeutic potential for messenger RNA (mRNA) in infectious diseases and cancer was first realized almost three decades ago, but only in 2018 did the first lipid nanoparticle-based small interfering RNA (siRNA) therapy reach the market with the United States Food and Drug Administration (FDA) approval of patisiran (Onpattro™) for hereditary ATTR amyloidosis. This was largely made possible by major advances in the formulation technology for stabilized lipid-based nanoparticles (LNPs). Design of the cationic ionizable lipids, which are a key component of the LNP formulations, with an acid dissociation constant (pKa) close to the early endosomal pH, would not only ensure effective encapsulation of mRNA into the stabilized lipoplexes within the LNPs, but also its subsequent endosomal release into the cytoplasm after endocytosis. Unlike other gene therapy modalities, which require nuclear delivery, the site of action for exogenous mRNA vaccines is the cytosol where they get translated into antigenic proteins and thereby elicit an immune response. LNPs also protect the mRNA against enzymatic degradation by the omnipresent ribonucleases (RNases). Cationic nano emulsion (CNE) is also explored as an alternative and relatively thermostable mRNA vaccine delivery vehicle. In this review, we have summarized the various delivery strategies explored for mRNA vaccines, including naked mRNA injection; ex vivo loading of dendritic cells; CNE; cationic peptides; cationic polymers and finally the clinically successful COVID-19 LNP vaccines (Pfizer/BioNTech and Moderna vaccines)-their components, design principles, formulation parameter optimization and stabilization challenges. Despite the clinical success of LNP-mRNA vaccine formulations, there is a specific need to enhance their storage stability above 0 °C for these lifesaving vaccines to reach the developing world.

Evaluation of the feasibility and acceptability of ReWin-A digital therapeutic rehabilitation innovation for people with stroke-related disabilities in India.

Background: Developing culturally appropriate, scalable interventions to meet the growing needs for stroke rehabilitation is a significant problem of public health concern. Therefore, systematic development and evaluation of a scalable, inclusive, technology-driven solution for community-based stroke care are of immense public health importance in India. ReWin is a digital therapeutics platform that was developed systematically. This study aimed to evaluate its feasibility and acceptability in an Indian context. Objectives: Phase-1: To pilot the intervention for identifying operational issues and finalize the intervention. Phase-2: To assess the feasibility and acceptability of ReWin intervention in an Indian context. Methods: Design: Mixed-methods research design. Setting: Participant's home and rehabilitation centers. Participants were selected from rehabilitation centers in South India. Participants: Ten stroke survivors and their caregivers, as well as four rehabilitation service providers were recruited for phase 1. Thirty stroke survivors who were treated and discharged from the hospital, and their caregivers as well as 10 rehabilitation service providers were recruited for Phase 2. Intervention: ReWin a digital therapeutic platform with the provider and patient app for the rehabilitation of physical disabilities following stroke was piloted. Process: Evaluation of the intervention was completed in two phases. In the first phase, the preliminary intervention was field-tested with 10 stroke survivors and four rehabilitation service providers for 2 weeks. In the second phase, the finalized intervention was provided to a further 30 stroke survivors to be used in their homes with support from their carers as well as to 10 rehabilitation service providers for 4 weeks. Outcome measures: Primary outcomes: (1) operational difficulties in using the ReWin intervention; (2) feasibility and acceptability of the ReWin intervention in an Indian setting. Results: Field-testing identified operational difficulties related to 1. Therapeutic content; 2. Format; 3. Navigation; 4. Connectivity, 5. Video-streaming, 6. Language; and 7. Comprehensibility of the animated content. The intervention was reviewed, revised and finalized before pilot testing. Findings from the pilot testing showed that the ReWin intervention was feasible and acceptable. About 76% of the participants had used ReWin for more than half of the intervention period of 4 weeks. Ninety percentage of the stroke care providers and about 60% of the stroke survivors and caregivers felt that the content of ReWin was very relevant to the needs of the stroke survivors. Forty percentage of the stroke survivors and caregivers rated ReWin intervention as excellent. Another 45% of the stroke survivors and caregivers as well as 90% of the stroke care providers rated ReWin intervention as very good based on its overall credibility, usability, and user-friendliness. Conclusions: ReWin has all the essential components to connect care providers and consumers not just for stroke rehabilitation but for several other health conditions with the use of several other technological features that support rehabilitation of persons with disabilities and strengthen rehabilitation in health systems worldwide. It is critical to amalgamate ReWin and other evidence-based interventions for rehabilitation to innovate scalable solutions and promote universal health coverage for stroke care worldwide.

Entry level nursing graduate students' perception and readiness toward online component of blended learning: A mixed method study.

BACKGROUND: Blended learning (BL), the integration of online with face to face teaching, is established as a teaching method in higher education. Understanding the learner's readiness toward online component of BL is important in designing and delivering BL. Nursing students require proficiency in interpersonal relationship and social interaction apart from knowledge and skills. BL may provide an opportunity to acquire the professional skills better than the traditional face to face sessions. The objectives of this study were to identify the nursing student's readiness toward BL and perceptions about the online learning component of BL. MATERIALS AND METHODS: First- and second-year entry level graduate nursing students of Sri Ramachandra Institute of Higher Education, Chennai, India, were the participants of the study. This study used a mixed method approach. An online questionnaire, developed based on the literature and expert consensus, was used in the first phase. A focus group discussion (FGD) with ten random participants of the survey was conducted to understand the perceptions and readiness to adopt the online component of BL. The present study was conducted from December 2019 to January 2020. Survey results were analyzed through descriptively. Content analysis was carried to summarize FGD results. RESULTS: A total 158 students of entry level nursing graduate programme participated in the survey. 53.8% of felt BL will have positive effect on their learning and 70% of the respondents were ready to adopt BL. The FGD identified two themes: (I) Readiness to adopt online learning as a component of BL and (II) perceived barriers and challenges in adopting online contents. CONCLUSION: Entry level nursing graduate students had a positive perception about the online components. Majority of them are confident in accessing the online contents. Willingness to learn through online, previous experience with online learning, and perceived advantages of online component might influence the learner's readiness. Availability of internet and absence of teachers were perceived as the barriers to online learning by the participants.

Blended learning and health professional education: Protocol for a mixed-method systematic review.

Blended learning (BL) refers to a systematic teaching method, which combines the aspects of face-to-face and online interactions using appropriate Information and Communication Technologies. This mixed-method systematic review (SR) protocol is developed with the objective to determine the effectiveness and appropriateness of BL in the health-care professional education. Mixed-method SR protocol: For the purpose of this SR, PICO is defined as P-entry level graduate students of health sciences program; I-BL; C-traditional face-to-face training; and O-achievement of learning outcomes, learner's and teacher's perception (primary). The search will be done through possible database using predetermined search strategy. Eligible studies will be appraised independently by authors. Joanna Briggs Institute's mixed-method protocol will be used to assess and synthesis the data. This protocol is registered with the International Register of Systematic Reviews (PROSPERO) with the registration number CRD42018082699.

Apical ballooning syndrome in first degree relatives.

Apical ballooning syndrome (Takotsubo cardiomyopathy) is an unusual stress-related reversible cardiomyopathy occurring commonly in postmenopausal females. Genetic etiology of this condition is uncertain. A 68-year-old female and her daughter aged 43 got admitted to our institute simultaneously with acute chest pain following demise of one of their close relative. Both had features typical of Takotsubo cardiomyopathy and recovered completely. This reports point to the possible genetic predisposition to this abnormality.

Effects of chain length on oligopeptide hydrogelation.

The co-assembly of mutually complementary, but self-repulsive oligopeptide pairs into viscoelastic hydrogels has been studied. Oligopeptides of 6, 10, and 14 amino acid residues were used to investigate the effects of peptide chain length on the structural and mechanical properties of the resulting hydrogels. Biophysical characterizations, including dynamic rheometry, small-angle X-ray scattering (SAXS) and fluorescence spectroscopy, were used to investigate hydrogelation at the bulk, fiber, and molecular levels, respectively. Upon mixing, the 10-mer peptides and the 14-mer peptides both form hydrogels while the 6-mer peptides do not. SAXS studies point to morphological similarity of the cross-sections of fibers underlying the 10:10 and 14:14 gels. However, fluorescence spectroscopy data suggest tighter packing of the amino acid side chains in the 10:10 fibers. Consistent with this tighter packing, dynamic rheometry data show that the 10:10 gel has much higher elastic modulus than the 14:14 mer (18 kPa vs. 0.1 kPa). Therefore, from the standpoint of mechanical strength, the optimum peptide chain length for this class of oligopeptide-based hydrogels is around 10 amino acid residues.

Effect of temperature during assembly on the structure and mechanical properties of peptide-based materials.

Mutually complementary, self-repulsive oligopeptide pairs were designed to coassemble into viscoelastic hydrogels. Peptide engineering was combined with biophysical techniques to investigate the effects of temperature on the structural and mechanical properties of the resulting hydrogels. Biophysical characterizations, including dynamic rheometry, small-angle X-ray scattering (SAXS), and fluorescence spectroscopy, were used to investigate hydrogelation at the bulk, fiber, and molecular levels, respectively. It has been found that temperature has a significant effect on the structure and mechanical properties of peptide-based biomaterials. Oligopeptide fibers assembled at 25 degrees C are formed faster and are two times thicker, and the resulting material is mechanically seven times stronger than that assembled at 5 degrees C.

Peptide-based viscoelastic matrices for drug delivery and tissue repair.

Molecular self-assembly has paved the way to create novel, supramolecular, functional biomaterials. Peptide-based biomaterials are gaining interest as a result of their programmability, biodegradability, and bioresorbability. Further, unlike polymeric materials, peptides can be made monodisperse with precise control over sequence, chain length, and stereochemistry. Peptide-based viscoelastic matrices have been designed and characterized for various biomedical applications, such as tissue engineering scaffolds or drug delivery vehicles. The 'holy grail' in designing an ideal tissue engineering scaffold lies in mimicking the cues of the tissue's natural extracellular matrix (ECM). Some of the key elements of ECM that are incorporated into these peptide scaffolds include cell-adhesive and protease-sensitive sequences for enhanced cell-cell and cell-biomaterial interactions. Peptide-based viscoelastic matrices can also be engineered with drug carrying protease-sensitive sequences for controlled and site-specific drug delivery. Molecular-level engineering of simple oligopeptide modules can be used to control the position and density of the bio-mimetic functionalities in the supramolecular structures, which demonstrates the power of the 'bottom-up' approach in self-assembly.

Repeated rapid shear-responsiveness of peptide hydrogels with tunable shear modulus.

A pair of mutually attractive but self-repulsive decapeptides, with alternating charged/neutral amino acid sequence patterns, was found to co-assemble into a viscoelastic material upon mixing at a low total peptide concentration of 0.25 wt %. Circular dichroism spectroscopy of individual decapeptide solutions revealed their random coil conformation. Transmission electron microscopy images showed the nanofibrillar network structure of the hydrogel. Dynamic rheological characterization revealed its high elasticity and shear-thinning nature. Furthermore, the co-assembled hydrogel was capable of rapid recoveries from repeated shear-induced breakdowns, a property desirable for designing injectable biomaterials for controlled drug delivery and tissue engineering applications. A systematic variation of the neutral amino acids in the sequence revealed some of the design principles for this class of biomaterials. First, viscoelastic properties of the hydrogels can be tuned through adjusting the hydrophobicity of the neutral amino acids. Second, the beta-sheet propensity of the neutral amino acid residue in the peptides is critical for hydrogelation.