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Kaṇakkatikāram is the title of elementary mathematical treatises that focus on measurements, calculation techniques and practical-recreational word problems. These treatises enjoyed substantial distribution in medieval and colonial Tamil Nadu and Kerala (Parameswara Iyer 1990, Vol. 2, 524-527). In this paper we will describe these treatises based on Malayalam manuscripts. We will discuss their content, linguistic and stylistic form, context of use, relation to actual professional practices, the cultural values that they express, and the political-economic reality that they reflect. Since the Tamil versions have already been analyzed in Senthil Babu (2022), here we focus on aspects that complement his analysis and on features that are unique to, or more salient in, the Malayalam versions.

Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate is the late diagnosis of cancer development. Although traditional approaches to diagnosing HCC have become gold-standard, there remain several limitations due to which the confirmation of cancer progression takes a longer period. The recent emergence of artificial intelligence tools with the capacity to analyze biomedical datasets is assisting traditional diagnostic approaches for early diagnosis with certainty. Here we present a review of traditional HCC diagnostic approaches versus the use of artificial intelligence (Machine Learning and Deep Learning) for HCC diagnosis. The overview of the cancer-related databases along with the use of AI in histopathology, radiology, biomarker, and electronic health records (EHRs) based HCC diagnosis is given.

Dopamine in the ventral striatum conveys a complex reward-associated signal. This study demonstrates how dopamine reflects sustained reward availability, and rapidly signals unexpected changes in availability.

This study focused on the perception of the students on entrepreneurship education. Entrepreneurship education is considered an effective tool for influencing students' learning orientation and expression. This study examined the effects of entrepreneurship education and learning on the entrepreneurial implementation intentions of students at the Universities. This study employed an explanatory method. For the study, 600 questionnaires were collected from north Indian university students. Multiple regression was used for the analysis of the study. The results showed that teaching methods significantly impact entrepreneurship stimulate students' interest and enhance students' knowledge innovation for business start-ups. The findings of the analysis also revealed that practical activities are mainly based on vocational skill activities, the teaching methods should contain extensive attention to critical thinking and idea generation activities as graded mechanisms of the degree program. It was also recommended that engagement of students with entrepreneurial development initiatives provided by institutions should involve students across all degree levels. Therefore, to increase the prospect of assignation in entrepreneurial activities after graduation students should generate viable business ideas, identify market gaps, engage in business startups, and practical business plans, and engage in invention and innovations. [Note: The page range (143-156) shown on the PDF is incorrect. The correct page range is 143-155.]

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Study objectiveMost long coronavirus disease (long COVID) studies rely on traditional surveillance methods that miss underserved populations who use emergency departments (EDs) as their primary health care source. In medically underserved ED populations, we sought to determine (1) whether there are gaps in awareness and self-declared understanding about long COVID illness, and (2) the prevalence, impact on school/work attendance, and receipt of care for long COVID symptoms.MethodsThis study was a cross-sectional, convenience sample survey study of adult patients at 11 geographically representative US EDs from December 2022 to October 2023. Awareness and self-declared understanding about long COVID illness were measured. Prevalence, impact on school/work attendance, and receipt of care for long COVID symptoms were also assessed.ResultsOf 1,618 eligible patients, 1455 (89.9%) agreed to participate, including 33.4% African Americans and 30.9% Latino/a. Of the patients, 17.1% lacked primary care. In total, 33.2% had persistent COVID-19 symptoms lasting >1 month, and 20.3% had symptoms >3 months. Moreover, 49.8% with long COVID symptoms missed work/school because of symptoms; 30.3% of all participants and 33.5% of participants who had long COVID symptoms had prior awareness and self-declared understanding of long COVID. Characteristics associated with poor understanding of long COVID were African American race (adjusted odds ratio [aOR] 3.68, 95% confidence interval [CI] 2.66 to 5.09) and Latino/a ethnicity (aOR 3.16, 95% CI 2.15 to 4.64). Participants lacking primary care were less likely to have received long COVID care (24.6% versus 51.2%; difference 26.6%; 95% CI 13.7% to 36.9%).ConclusionsDespite high prevalence and impact on school/work attendance of long COVID symptoms, most of this ED population had limited awareness and self-declared understanding of long COVID, and many had not received care. EDs should consider the development of protocols for diagnosis, education, and treatment of long COVID illness.

To facilitate our understanding of proteome dynamics during signaling events, robust workflows affording fast time resolution without confounding factors are essential. We present Surface-exposed protein Labeling using PeroxidaSe, H2O2, and Tyramide-derivative (SLAPSHOT) to label extracellularly exposed proteins in a rapid, specific, and sensitive manner. Simple and flexible SLAPSHOT utilizes recombinant soluble APEX2 protein applied to cells, thus circumventing the engineering of tools and cells, biological perturbations, and labeling biases. We applied SLAPSHOT and quantitative proteomics to examine the TMEM16F-dependent plasma membrane remodeling in WT and TMEM16F KO cells. Time-course data ranging from 1 to 30 min of calcium stimulation revealed co-regulation of known protein families, including the integrin and ICAM families, and identified proteins known to reside in intracellular organelles as occupants of the freshly deposited extracellularly exposed membrane. Our data provide the first accounts of the immediate consequences of calcium signaling on the extracellularly exposed proteome.

Chromium and arsenic are two of the most problematic water pollutants due to their high toxicity and prevalence in various water streams. While adsorption and ion-exchange processes have been applied for the efficient removal of numerous toxic contaminants, including heavy metals, from water, these technologies display relatively low overall performances and stabilities for the remediation of chromium and arsenic oxyanions. This work presents the use of polyol-functionalized porous aromatic framework (PAF) adsorbent materials that use chelation, ion-exchange, redox activity, and hydrogen-bonding interactions for the highly selective capture of chromium and arsenic from water. The chromium and arsenic binding mechanisms within these materials are probed using an array of characterization techniques, including X-ray absorption and X-ray photoelectron spectroscopies. Adsorption studies reveal that the functionalized porous aromatic frameworks (PAFs) achieve selective, near-instantaneous (reaching equilibrium capacity within 10 s), and high-capacity (2.5 mmol/g) binding performances owing to their targeted chemistries, high porosities, and high functional group loadings. Cycling tests further demonstrate that the top-performing PAF material can be recycled using mild acid and base washes without any measurable performance loss over at least ten adsorption-desorption cycles. Finally, we establish chemical design principles enabling the selective removal of chromium, arsenic, and boron from water. To achieve this, we show that PAFs appended with analogous binding groups exhibit differences in adsorption behavior, revealing the importance of binding group length and chemical identity.

BACKGROUND: Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape. OBJECTIVE: The objectives of this study were to (1) validate MT for measuring low back posture and movement and (2) assess the acceptability of MT for users. METHODS: A total of 10 participants without LBP were tested. A 3D optical motion capture system was used as a reference standard to measure low back kinematics. Retroreflective markers and a matrix of MTs were placed on the low back to measure kinematics (motion capture) and strain (MT) simultaneously during low back movements in the sagittal, frontal, and axial planes. Cross-correlation coefficients were calculated to evaluate the concurrent validity of MT strain in reference motion capture kinematics during each movement. The acceptability of MT was assessed using semistructured interviews conducted with each participant after laboratory testing. Interview data were analyzed using rapid qualitative analysis to identify themes and subthemes of user acceptability. RESULTS: Visual inspection of concurrent MT strain and kinematics of the low back indicated that MT can distinguish between different movement directions. Cross-correlation coefficients between MT strain and motion capture kinematics ranged from -0.915 to 0.983, and the strength of the correlations varied across MT placements and low back movement directions. Regarding user acceptability, participants expressed enthusiasm toward MT and believed that it would be helpful for remote interventions for LBP but provided suggestions for improvement. CONCLUSIONS: MT was able to distinguish between different low back movements, and most MTs demonstrated moderate to high correlation with motion capture kinematics. This preliminary laboratory validation of MT provides a basis for future device improvements, which will also involve testing in a free-living environment. Overall, users found MT acceptable for use in physical therapy for managing LBP.

The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory, and dynamical mean-field theory calculations, we visualize a fourfold degenerate Dirac nodal line at the boundary of the bulk Brillouin zone in the antiferromagnet YMn2Ge2. We further demonstrate that this gapless, antiferromagnetic Dirac nodal line is enforced by the combination of magnetism, space-time inversion symmetry, and nonsymmorphic lattice symmetry. The corresponding drumhead surface states traverse the whole surface Brillouin zone. YMn2Ge2 thus serves as a platform to exhibit the interplay of multiple degenerate nodal physics and antiferromagnetism. Interestingly, the magnetic nodal line displays a d-orbital dependent renormalization along its trajectory in momentum space, thereby manifesting Hund's coupling. Our findings offer insights into the effect of electronic correlations on magnetic Dirac nodal lines, leading to an antiferromagnetic Hund nodal line.

Since extracellular vesicles (EVs) have emerged as a promising drug delivery system, diverse methods have been used to load them with active pharmaceutical ingredients (API) in preclinical and clinical studies. However, there is yet to be an engineered EV formulation approved for human use, a barrier driven in part by the intrinsic heterogeneity of EVs. API loading is rarely assessed in the context of single vesicle measurements of physicochemical properties but is likely administered in a heterogeneous fashion to the detriment of a consistent product. Here, we applied a suite of single-particle resolution methods to determine the loading of rhodamine 6G (R6G) surrogate cargo mimicking hydrophilic small molecule drugs across four common API loading methods: sonication, electroporation, freeze-thaw cycling and passive incubation. Loading efficiencies and alterations in the physical properties of EVs were assessed, as well as co-localization with common EV-associated tetraspanins (i.e., CD63, CD81 and CD9) for insight into EV subpopulations. Sonication had the highest loading efficiency, yet significantly decreased particle yield, while electroporation led to the greatest number of loaded API particles, albeit at a lower efficiency. Moreover, results were often inconsistent between repeated runs within a given method, demonstrating the difficulty in developing a rigorous loading method that consistently loaded EVs across their heterogeneous subpopulations. This work highlights the significance of how chosen quantification metrics can impact apparent conclusions and the importance of single-particle characterization of EV loading.

Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.

The tensile creep behavior of a vacuum arc-melted Nb45Ta25Ti15Hf15 refractory high entropy alloy was investigated over a constant true stress range of 50–300 MPa at a temperature of 1173 K. Creep tests were carried out in both high vacuum (5 × 10−6 torr) and ultrahigh purity Ar gas to examine the environmental effect. The samples tested in vacuum exhibited power law behavior with a stress exponent of 4.1 and exceptional tensile creep ductility, whereas those tested in Ar suffered significant embrittlement due to HfO2 formation at grain boundaries, which was exacerbated at low applied stresses where extended exposure to residual O2 gas resulted in more extensive brittle intergranular fracture. Phase decomposition occurred after long-term thermal exposure, where a second Hf-rich body-centered cubic phase formed predominantly at grain boundaries but did not cause embrittlement. Compared to the equiatomic TaNbHfZrTi (Senkov alloy) and face-centered cubic multiple-principal element alloys, Nb45Ta25Ti15Hf15 has superior creep resistance, especially at high applied stresses, while maintaining excellent creep ductility. Transmission electron microscopy revealed that creep deformation in Nb45Ta25Ti15Hf15 at 1173 K is controlled by cross-kink collisions from screw dislocations that results in dipole drag at lower strain rates and jog drag at higher strain rates.

Background and Objectives: Ischemic stroke (IS) in young is increasingly recognized as an important health problem in low- and middle-income countries (LMICs). Limited data is available from LMICs about the predictors of outcome and recurrence of IS in young. The study aims to assess the predictors of short-term and long-term functional outcome and the recurrence of the first-ever IS and transient ischemic attack in young. Methods: The clinical, radiological data, functional outcome, and recurrence of 569 IS patients aged 18-50 years were recorded. The etiological classification was done. The functional outcome was assessed using the modified Rankin scale (mRS), with a good outcome being mRS 0-2. Logistic regression was used to analyze the predictors of recurrence and outcome. Results: The most common risk factors were hypertension (40.3%) and diabetes mellitus (34.3%). Stroke of undetermined etiology (33.6%) was the most common etiological subtype. Among the cardioembolic subtypes, rheumatic heart disease constituted 47.4%. A good functional outcome at 1 year was seen in 76.3%. While baseline stroke severity predicted both the 3-month and 1-year functional outcome, age ≥40 years and male gender predicted poor outcome at 1 year. The risk of recurrent strokes at 1 year was 2.5%. On multiple logistic regression analysis, diabetes mellitus independently predicted recurrent vascular events at 1 year (odds ratio = 2.43, 95% confidence interval 1.07-5.50). Conclusions: We found a good functional outcome with a relatively low recurrence at 1 year among young IS patients. Baseline stroke severity, age, and male gender predicted a poor 1-year functional outcome. Keywords: Young stroke, ischemic, recurrence, outcome, Author(s): Balaswamy Reddy [1]; Arun Kathuveetil [2]; Sapna Erat Sreedharan [1]; VT Jissa [3]; PN Sylaja (corresponding author) [1] Introduction In developing countries, the epidemiologic trends of young ischemic stroke [...]

Background and Objectives: Cardiovascular magnetic resonance imaging (CMRI) is the noninvasive technique of choice for early detection of cardiac involvement in Duchenne and Becker muscular dystrophy (DMD and BMD, respectively), but is seldom used in routine clinical practice in the Indian context. We sought to determine the prevalence of CMRI abnormalities in patients with DMD and BMD and to compare the CMRI parameters with the phenotypic and genotypic characteristics. Methods: A prospective, observational study was conducted on patients genetically diagnosed with DMD and BMD who could complete CMRI between March 2020 and March 2022. Abnormal CMRI was the presence of any late gadolinium enhancement (LGE) that signifies myocardial fibrosis (LGE positivity), regional wall motion abnormality, or reduced left ventricular ejection fraction (LVEF Results: A total of 46 patients were included: 38 patients with DMD and eight with BMD. Cardiac abnormality was seen in 23 (50%) patients. LGE was more common than impaired LVEF in DMD (16, 42.1%), while impaired LVEF was more common in BMD (5, 62.5%). LGE was most frequently found in lateral wall (18/19) followed by inferior (6/19), septal (5/19), anterior (2/19), and apex (1/19). Among the various clinicodemographic parameters, only age (r = 0.495, P = 0.002) and disease duration (r = 0.407, P = 0.011) were found to significantly correlate with LGE in patients with DMD. No association was found between the various CMRI parameters and the genotype. Conclusions: The current study highlights the differences in myocardial fibrosis and LV dysfunction between DMD and BMD, along with other CMRI parameters. Notably, a genotype-CMRI correlation was not found in the current cohort, which needs to be further explored. Keywords: Dystrophinopathy, Duchenne muscular dystrophy, Becker muscular dystrophy, cardiomyopathy, cardiac MRI, Author(s): Manu Santhappan Girija [1]; Deepak Menon (corresponding author) [1]; Kiran Polavarapu [1,2]; Veeramani Preethish-Kumar [1]; Seena Vengalil [1]; Saraswati Nashi [1]; Madassu Keertipriya [1]; Mainak Bardhan [1]; Priya Treesa [...]