Efficacy of anti-suppression therapy in improving binocular vision in children with small-angle Esotropia

Purpose: To evaluate the efficacy of anti-suppression exercises in children with small-angle esotropia in achieving binocular vision. Methods: A retrospective review of patients aged 3–8 years who underwent anti-suppression exercises for either monocular or alternate suppression between January 2016 and December 2021 was conducted. Patients with esotropia less than 15 prism diopters (PD) and visual acuity ≥ 6/12 were included. Patients with previous intra-ocular surgery or less than three-month follow-up were excluded. Success was defined as the development of binocular single vision (BSV) for distance, near, or both (measured clinically with either the 4 prism base out test or Worth four dot test) and maintained at two consecutive visits. Qualified success was defined as the presence of diplopia response for both distance and near. Additionally, improvement in near stereo acuity was measured using the Stereo Fly test. Results: Eighteen patients with a mean age of 5.4 ± 1.38 years (range 3–8 years) at the time of initiation of exercises were included in the study. The male female ratio was 10:8. The mean best corrected visual acuity was 0.18 LogMAR unit(s) and the mean spherical equivalent was +3.8 ± 0.14 diopters (D). The etiology of the esotropia was fully accommodative refractive esotropia (8), microtropia (1), post–operative infantile esotropia (4), partially accommodative esotropia (1), and post-operative partially accommodative esotropia (4). Patients received either office-based, home-based, or both modes of treatment for an average duration of 4.8 months (range 3–8). After therapy, BSV was achieved for either distance or near in 66.6 % of patients (95 % CI = 40.03–93.31 %). Binocular single vision for both distance and near was seen in 50 % of children. Qualified success was observed in 38.46% of patients. Persistence of suppression was observed in one patient (5.5 %)... (AU)

[Coronary heart disease: innovative understanding from traditional Chinese medicine and treatment by classic formulas].

Coronary heart disease is a common cardiovascular disease, attacking about 11.4 million patients in China. With increasing prevalence and mortality year by year, coronary heart disease has become a major factor threatening human health and public health. Although primary and secondary prevention, intervention, coronary artery bypass grafting and other interventions have reduced the death rate, there are drug(aspirin) resistance, secondary nitroglycerin failure, post-intervention fatigue, chest tightness, and an-xiety, and complication with a high risk of bleeding, which have become the key clinical and scientific issues needed to be resolved. Coronary heart disease belongs to the category of chest impediment and heart pain in traditional Chinese medicine(TCM). The TCM etiology of this disease includes external contraction of cold, emotional disorders, constitutional insufficiency, physical weakness, and labor injury, which are closely related to sympathetic nerve activity, state of cardiac and psychological diseases, family history, and cardiovascular metabolic disorders in modern medicine. The TCM causes of coronary heart disease include Qi depression, phlegm turbidity, blood stasis, fire-heat, cold congealing, and healthy Qi deficiency, which are associated with emotional factors such as anxiety and depression, abnormal lipid metabolism, abnormalities in blood circulation and coagulation, inflammatory responses, hyperactive immune responses, and heart failure, chronic wasting disease, or aging, respectively. Accordingly, the patients with Qi depression should be treated with Chaihu Longgu Muli Decoction, and those with phlegm turbidity should be treated with Wendan Decoction and Gualou Xiebai Banxia Decoction. Xuefu Zhuyu Decoction and Guizhi Fuling Pills are recommended for blood stasis, Xiaoxianxiong Decoction and Sanhuang Xiexin Decoction for fire-heat, Zhishi Xiebai Guizhi Decoction for cold congealing, and Renshen Decoction for healthy Qi deficiency. Due to the changes in the spectrum of diseases from ancient to modern times as well as the differences in physical constitution, the key cause of coronary heart disease has evolved from the chest Yang deficiency and cold congealing to Qi depression, phlegm turbidity, phlegm combined with stasis, and fire-heat, showing a shift from cold to heat and from deficiency to excess. The combination of classic formulas presents a pattern. That is, the core formula-syndrome correspondence of a disease often fixedly appears with other formula-syndrome correspondence, which may be related to the development of the pathophysiological mechanism of the disease. In the clinical application of modern pharmacological results, the research team has formulated the clinical principle of pathogenesis corresponding to pathological changes and medicinal nature corresponding pharmacological effects. The modern pharmacological research on classic formulas is conducive to targeted treatment. Moreover, classic formulas help to ameliorate aspirin resistance, clopidogrel resistance, post-intervention anxiety, and high risk of bleeding and address the lack of effective blockade of critical lesions in the coronary artery and the progression of post-infarction heart failure. The innovative understanding of the etiology and pathogenesis of co-ronary heart disease helps to improve the clinical efficacy of TCM and the clinical system for treating coronary heart disease with classic formulas.

An investigation on the prevalence and patterns of multi-morbidity among a group of slum-dwelling older women of Kolkata, India.

BACKGROUND: Multi-morbidity is a pervasive and growing issue worldwide. The prevalence of multi-morbidity varies across different populations and settings, but it is particularly common among older adults. It poses substantial physical, psychological, and socio-economic burdens on individuals, caregivers and healthcare systems. In this context, the present study aims to provide an insight on the prevalence and degree of multi-morbidity; and also, on the relationship between level of multi-morbidity and morbid conditions among a group of slum-dwelling older women. METHODS: This community based cross-sectional study was conducted in the slum areas of urban Kolkata, West Bengal, India. It includes total 500 older women, aged 60 years or above. Pre-tested schedules on so-demographic and morbidity profile have canvassed to obtain the information by door-to-door survey. To determine the relationship between the level of multi-morbidity and morbid conditions, correspondence analysis has performed. RESULTS: The study revealed three most prevalent morbid conditions- back and/or joint pain, dental caries/cavity and hypertension. The overall prevalence of multi-morbidity was 95.8% in this group of older women. It was highly over-represented by the oldest-old age group (80 years and above). Majority were found to suffer from five simultaneous morbid conditions that accounted for 15.2% of the total respondents. All of the oldest-old women of this study reported to suffer from more than two medical conditions simultaneously. Three distinct groups were formed based on the inter-relationship between level of multi-morbidity and morbid conditions. The group 1 and 2 represents only 27.8% and 18% of the total sample. Whereas, group 3 comprises the highest level of morbidities (≥ 6) and 52.8% of total sample, and strongly related with general debilities, cardiac problems, asthma/COPD, gastrointestinal, musculoskeletal problems, neurological disorders, hypothyroidism and oral health issues. CONCLUSION: The findings confirmed the assertion that multi-morbidity in slum living older adults is a problem with high prevalence and complexity. This study proposes an easily replicable approach of understanding complex interaction of morbidities that can help further in identifying the healthcare needs of older adults to provide them with healthy and more productive life expectancy.

Stability analysis of patient-specific 4DCT- and 4DCBCT-based correspondence models.

BACKGROUND: Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondence model. PURPOSE: This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinical parameters. METHODS: For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners). RESULTS: Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of 0.6 / 0.7 $0.6/ 0.7$ between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold of D S C > 0.7 $DSC > 0.7$ for the GTV, and only 14/46 patients demonstrating a D S C > 0.7 $DSC > 0.7$ in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is 0.59 ± 0.26 $0.59\pm 0.26$ (mean ASSD of 2.83 ± 3.37 $2.83\pm 3.37$ ) and the respective ITV-based DSC is 0.69 ± 0.20 $0.69\pm 0.20$ (mean ASSD of 2.35 ± 1.81 $2.35\pm 1.81$ ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increased stability. CONCLUSIONS: The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course of treatment.

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis.

OBJECTIVE: Modern transportation amenities and lifestyles have changed people's behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. METHODS: Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. RESULTS: CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. CONCLUSION: The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Altitudinal Impact on Phytochemical Composition and Mycorrhizal Diversity of Taxus Contorta Griff in the Temperate Forest of Shimla District.

Taxus contorta (family Taxaceae) is a native plant of temperate region of western Himalaya. The current study investigated the effect of altitude on the phytochemical composition and mycorrhizal diversity, associated with distribution of T. contorta in Shimla district, Himachal Pradesh, India. Quantitative phytochemical analysis of the leaf extracts indicated that alkaloid levels decreased with altitude, with the highest value in Himri's methanol extracts (72.79 ± 1.08 mg/g) while phenol content increased with altitude, peaking in Nankhari's methanol extracts (118.83 ± 5.90 mg/g). Saponin content was higher in methanol extracts (78.13 ± 1.66 mg/g in Nankhari, 68.06 ± 1.92 mg/g in Pabbas, and 56.32 ± 1.93 mg/g in Himri). Flavonoid levels were notably higher in chloroform extracts, particularly in Nankhari (219.97 ± 2.99 mg/g), and positively correlated with altitude. Terpenoids were higher in chloroform extracts at Himri (11.34 ± 0.10 mg/g) and decreased with altitude. Taxol content showed minimal variation between solvents and altitudes (4.53-6.98 ppm), while rutin was only detected in methanol extracts (1.31-1.46 ppm). Mycorrhizal spore counts in T. contorta's rhizosphere varied with altitude: highest at Himri (77.83 ± 2.20 spores/50 g soil), decreasing to Pabbas (68.06 ± 1.96 spores/50 g soil) and lowest at Nankhari (66.00 ± 2.77 spores/50 g soil), with 17 AMF species identified overall, showing significant altitudinal influence on spore density. The rhizosphere of T. contorta was shown to be dominated by the Glomus species. The rhizospheric soil of the plant was found to be slightly acidic. Organic carbon and available potassium content decreased contrasting with increasing available nitrogen and phosphorus with altitude. Correlation data showed strong negative links between organic carbon (-0.83), moderate positive for nitrogen (0.46) and phosphorus (0.414), and moderate negative for potassium (-0.56) with the altitude. This study provides a comprehensive insight into changes in phytochemical constituents, mycorrhizal diversity and soil composition of T. contorta along a range of altitude.

Investigating the effect of prescribing status and patient characteristics on the therapeutic outcomes in patients with diabetes using a leftover drug adjustment protocol.

Treatment for diabetes includes anti-diabetic medication in addition to lifestyle improvements through diet and exercise. In Japan, protocol-based pharmacotherapy management allows drug treatment to be provided through cooperation between physicians and pharmacists, based on a protocol that is prepared and agreed upon in advance. However, there are no studies to clarify the relationship between patient characteristics and therapeutic effects after pharmacist intervention in protocol-based pharmacotherapy management for patients with diabetes. Therefore, this study aimed to use protocol-based reports from pharmacies to understand the status of outpatient diabetes medication compliance. We classified patients with diabetes on the basis of patient characteristics that can be collected in pharmacies and investigated the characteristics that impacted diabetes treatment. Patients were prescribed oral anti-diabetic drugs at outpatient clinics of Hitachinaka General Hospital, Hitachi, Ltd., from April 2016 to March 2021. Survey items included patient characteristics (sex, age, number of drugs used, observed number of years of anti-diabetic drug prescription, number of anti-diabetic drug prescription days, and presence or absence of leftover anti-diabetic drugs) and HbA1c levels. Graphical analyses indicated the relationship between each categorised patient characteristic using multiple correspondence analyses. Subsequently, the patients were clustered using K-means cluster analysis based on the coordinates obtained for each patient. Patient characteristics and HbA1c values were compared between the groups for each cluster. A total of 1,910 patients were included and classified into three clusters, with clusters 1, 2, and 3 containing 625, 703, and 582 patients, respectively. Patient characteristics strongly associated with Cluster 1 were ages between 65 and 74 years, use of three or more anti-diabetic drugs, use of 3 years or more of anti-diabetic drugs, and leftover anti-diabetic drugs. Furthermore, Cluster 1 had the highest number of patients with worsening HbA1c levels compared with other clusters. Using the leftover drug adjustment protocol, we clarified the patient characteristics that affected the treatment course. We anticipate that through targeted interventions in patients exhibiting these characteristics, we can identify those who are irresponsibly continuing with drug treatment, are not responding well to therapy, or both. This could substantially improve the efficacy of their anti-diabetic care.

Liver respiratory-induced motion estimation using abdominal surface displacement as a surrogate: robotic phantom and clinical validation with varied correspondence models.

PURPOSE: This work presents the implementation of an RGB-D camera as a surrogate signal for liver respiratory-induced motion estimation. This study aims to validate the feasibility of RGB-D cameras as a surrogate in a human subject experiment and to compare the performance of different correspondence models. METHODS: The proposed approach uses an RGB-D camera to compute an abdominal surface reconstruction and estimate the liver respiratory-induced motion. Two sets of validation experiments were conducted, first, using a robotic liver phantom and, secondly, performing a clinical study with human subjects. In the clinical study, three correspondence models were created changing the conditions of the learning-based model. RESULTS: The motion model for the robotic liver phantom displayed an error below 3 mm with a coefficient of determination above 90% for the different directions of motion. The clinical study presented errors of 4.5, 2.5, and 2.9 mm for the three different motion models with a coefficient of determination above 80% for all three cases. CONCLUSION: RGB-D cameras are a promising method to accurately estimate the liver respiratory-induced motion. The internal motion can be estimated in a non-contact, noninvasive and flexible approach. Additionally, three training conditions for the correspondence model are studied to potentially mitigate intra- and inter-fraction motion.

Influence of learned landmark correspondences on lung CT registration.

BACKGROUND: Disease or injury may cause a change in the biomechanical properties of the lungs, which can alter lung function. Image registration can be used to measure lung ventilation and quantify volume change, which can be a useful diagnostic aid. However, lung registration is a challenging problem because of the variation in deformation along the lungs, sliding motion of the lungs along the ribs, and change in density. PURPOSE: Landmark correspondences have been used to make deformable image registration robust to large displacements. METHODS: To tackle the challenging task of intra-patient lung computed tomography (CT) registration, we extend the landmark correspondence prediction model deep convolutional neural network-Match by introducing a soft mask loss term to encourage landmark correspondences in specific regions and avoid the use of a mask during inference. To produce realistic deformations to train the landmark correspondence model, we use data-driven synthetic transformations. We study the influence of these learned landmark correspondences on lung CT registration by integrating them into intensity-based registration as a distance-based penalty. RESULTS: Our results on the public thoracic CT dataset COPDgene show that using learned landmark correspondences as a soft constraint can reduce median registration error from approximately 5.46 to 4.08 mm compared to standard intensity-based registration, in the absence of lung masks. CONCLUSIONS: We show that using landmark correspondences results in minor improvements in local alignment, while significantly improving global alignment.

A fully automatic fiducial detection and correspondence establishing method for online C-arm calibration.

PURPOSE: Online C-arm calibration with a mobile fiducial cage plays an essential role in various image-guided interventions. However, it is challenging to develop a fully automatic approach, which requires not only an accurate detection of fiducial projections but also a robust 2D-3D correspondence establishment. METHODS: We propose a novel approach for online C-arm calibration with a mobile fiducial cage. Specifically, a novel mobile calibration cage embedded with 16 fiducials is designed, where the fiducials are arranged to form 4 line patterns with different cross-ratios. Then, an auto-context-based detection network (ADNet) is proposed to perform an accurate and robust detection of 2D projections of those fiducials in acquired C-arm images. Subsequently, we present a cross-ratio consistency-based 2D-3D correspondence establishing method to automatically match the detected 2D fiducial projections with those 3D fiducials, allowing for an accurate online C-arm calibration. RESULTS: We designed and conducted comprehensive experiments to evaluate the proposed approach. For automatic detection of 2D fiducial projections, the proposed ADNet achieved a mean point-to-point distance of 0.65 ± 1.33 pixels. Additionally, the proposed C-arm calibration approach achieved a mean re-projection error of 1.01 ± 0.63 pixels and a mean point-to-line distance of 0.22 ± 0.12  mm. When the proposed C-arm calibration approach was applied to downstream tasks involving landmark and surface model reconstruction, sub-millimeter accuracy was achieved. CONCLUSION: In summary, we developed a novel approach for online C-arm calibration. Both qualitative and quantitative results of comprehensive experiments demonstrated the accuracy and robustness of the proposed approach. Our approach holds potentials for various image-guided interventions.

The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model.

In this article we demonstrate that dynamical quantum phase transitions (DQPTs) occur for an exemplary higher order topological insulator, the Benalcazar-Bernevig-Hughes model, following quenches across a topological phase boundary. A dynamical bulk boundary correspondence is also seen both in the eigenvalues of the Loschmidt overlap matrix and the boundary return rate. The latter is found from a finite size scaling analysis for which the relative simplicity of the model is crucial. Contrary to the usual two dimensional case the DQPTs in this model show up as cusps in the return rate, as for a one dimensional model, rather than as cusps in its derivative as would be typical for a two dimensional model. We explain the origin of this behaviour.

Walking to a number: is there affective involvement in generating the SNARC effect in numerical cognition?

The effect known as the spatial-numerical association of response codes (SNARC) documents fast reaction to small numbers with a response at the left and to large numbers with a response at the right. The common explanation appeals to a hypothetical mental number line of a left-to-right orientation with the numerical magnitudes on the line activated in an automatic fashion. To explore the possibility of emotional involvement in processing, we employed prototypical affective behaviors for responses in lieu of the usual spatial-numerical ones (i.e., of pressing lateralized keys). In the present series of experiments, the participants walked toward a number or walked away from a number (in a physical approach-avoidance setup) or said "good" or "bad" in response to a number. We recorded strong SNARC effects with affective responding. For example, it took participants longer to say "good" than "bad" to small numbers, but it took them longer to say "bad" than "good" to larger numbers. Although each particular outcome can still be accounted for by a spatial interpretation, the cumulative results are suggestive of the possibly of affective involvement in generating the effect.

Analysis of Movement and Actions of Wingers as Second-Line Players in Organized Attack in Handball.

In modern handball, one of the important performance indicators is the effectiveness of the attack, especially the running-in of wingers as line players which has not been explored adequately. The purpose of the study was to analyze the movements of wingers in the organized attack when they run in. Fifty-eight matches were analyzed from the 2022 EHF European Men's Handball Championship. A total of 491 attacks were recorded and 45 variables were analyzed in which wingers ran in as second pivots and the outcome was a throw. For the statistical analysis, descriptive and inductive statistics were used. The results showed that the average time of the running-in was 12.11 ± 9.28 s, the left wing ran in more often (60.1%) and wingers tended to move outside the defense formation (72.5%). Wingers ran in without the ball in possession (81.1%), moved toward defenders 2 and 5 and stood next to defenders, and occasionally blocked, slid or left their position. The defense's central zone was preferred by players to make a throw. Correspondence analysis showed that wingers finally returned to their initial position regardless of the evolution of the attack. Their role was to block a specific defender or disorganize the defense, for one defender to be isolated so that one attacking player could execute a shot from the central area. Conclusively, wingers play an important role, especially in run-in actions, at the completion of an attack, and in the final throw.

Letters generated by ChatGPT: Author who?

AIM: ChatGPT's role in medical writing is a topic of discussion. I experimented whether ChatGPT almost automatically generates Correspondence or Letter addressed to a "translated" article, and thereby wish to arouse discussion regarding ChatGPT use in medical writing. METHODS: I input an English article of mine into ChatGPT, tasking it with generating an English Disagreement Letter (Letter 1). Next, I tasked ChatGPT with translating the manuscript addressed to from English-French-Spanish-German. Then, I once again tasked ChatGPT with generating an English Disagreement Letter addressed to a German manuscript (triplicate translated manuscript) (Letter 2). RESULTS: Letters 1 and 2 are readable and reasonable, shooting the point that the author (myself) felt as the weakness of the article. Letters addressed to French (single translation) and to Spanish (double translation) and longer Letters (corresponding to Letters 1 and 2) are also readable, and thus stand. CONCLUSIONS: Solely based on this experiment, one may be able to write a letter even without understanding the meaning of the paper being addressed, let alone the language of the paper. Although this humble experiment does not conclude anything, I plea for a comprehensive discussion on the implications of these findings.

The role of correspondence analysis in medical research.

Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret "bi-plots," where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.

Separating spatial representations from polarity encoding in the processing of number and sequence stimuli in a four-way classification task.

Although the SNARC effect in the processing of most magnitude stimuli and sequence stimuli has been reported for the past 30 years, it remains unclear whether this effect is caused by the spatial representation or polarity encoding of stimuli. In the present study, we designed five experiments using a four-way classification task to evaluate the ability of spatial representation theory and polarity encoding theory to explain the SNARC effect in the processing of number and sequence stimuli. In all five experiments in the present study, stimuli (Experiments 1 and 4: four different Arabic numbers, Experiment 2: sequence stimuli, Experiment 3: ordinal sequences relevant to working memory, Experiment 5: Chinese characters without any implicit spatial information) were centrally presented. Participants were asked to respond to specific number or sequence stimuli by pressing the A, S, K, and L keys in consistent trials (or the L, K, S, and A keys in inconsistent trials). The results showed that (1) the SNARC effect occurred in the processing of number and sequence stimuli both when only one specific number was mapped to one specific key (Experiments 1, 2 and 3) and when two numbers were mapped to one specific key (Experiment 4). (2) There was not a SNARC effect when the numbers were replaced with Chinese characters without any implicit spatial information (Experiment 5). The results of these five experiments imply that the SNARC effect in the processing of magnitude stimuli, including numbers and sequences, originates from the spatial representation of stimuli, supporting spatial representation theory.

Anti-viral Effects of Pavetta indica Methanolic Extract and Acyclovir on Behavioral and Biochemical Parameters in Streptozotocin-induced Alzheimer's Disease in Rats.

BACKGROUND: Alzheimer's disease is a neurological dysfunction of the brain caused by neurodegeneration and oxidative stress. Some viruses, such as herpes viruses, HSV-1, and HSV-2, are causative agents of Alzheimer's disease and result in ß-amyloid peptide and tau protein accumulation in the brain. Some antiviral drugs, such as valacyclovir, acyclovir, and foscarnet, reduce amyloid-beta and P-tau. Pavetta indica leaves are also reported for their antiviral properties. The current study aimed to find out the significance of using Pavetta indica methanolic extract and acyclovir against Alzheimer's disease induced by streptozotocin. METHODS: Wistar rats received acyclovir and Pavetta indica methanolic extract orally at different dose ranges (50, 150, 450 mg/kg) and (125, 250, 500 mg/kg), respectively. The standard therapy, Rivastigmine (2 mg/kg), was given orally. RESULTS: Intracerebroventricular-streptozotocin produced significant alternations in behavioral assessments, including locomotor activity test, Morris water maze test, and elevated plus maze test. Moreover, intracerebroventricular-streptozotocin ameliorated the antioxidant defense activity by decreasing levels of catalase, superoxide dismutase, and reduced glutathione while enhancing the oxidative stress markers, including malondialdehyde, and total nitrite levels. Finally, the main findings showed that intracerebroventricular-streptozotocin significantly increased the inflammatory marker, tumor necrosis factor-α, and disturbed neurotransmitter mediators, including levels of acetylcholinesterase, glutamate, and γ-amino butyric acid. CONCLUSION: In a dose-dependent manner, acyclovir and Pavetta indica methanolic extract treatments abrogated the streptozotocin-induced behavioral and neurological abnormalities in rats. The potential therapeutic effects of PIME and acyclovir administration in intracerebroventricular-streptozotocin-treated rats may be attributed to its potential antiviral, antioxidant, and anti-inflammatory effects. The current study suggests that Pavetta indica methanolic extract and acyclovir are promising therapeutic targets against Alzheimer's disease.

Comparison of Impressions of COVID-19 Vaccinations Stratified by the Number of Vaccinations Among Japanese Healthcare Professional University Students.

OBJECTIVE: Coronavirus infectious disease, that emerged in 2019 (COVID-19) has been a major public health issue not only in Japan, but also worldwide, and the implementation of a proper vaccination strategy has been important. To promote vaccination, the present study compared impressions of COVID-19 vaccinations stratified by the number of vaccinations among healthcare professional university students in Okayama, Japan, and suggests better vaccination strategies. METHOD: A total of 212 Japanese healthcare professional university students were enrolled in this clinical qualitative study using the text mining method. A self-reported questionnaire, including questions such as "What do you think about COVID-19 vaccinations?" was performed. We also examined the number of vaccinations, sex, history of COVID-19 infection, and daily mask use. RESULTS: A total of 5,935 words were obtained and "Think" (169 times) was the most frequently used followed by "Inject" (108 times), "Inoculation" (97 times), "Vaccine" (83 times), "Corona" (66 times) and "Side effects" (49 times). Characteristic words were "Safety" in non-vaccinated subjects and "Side effects" and "Necessary" in vaccinated subjects. In addition, "Safety" in non-vaccinated men and "Frightening" in non-vaccinated women were characteristic and fundamental features. CONCLUSION: Impressions of COVID-19 vaccinations stratified by the number of vaccinations differed among healthcare professional university students. The provision of appropriate information on safety to non-vaccinated subjects and side effects to vaccinated subjects appears to be necessary. In addition, sex-specific information may be required for non-vaccinated subjects.

Preliminary investigation on the causes of red tides in Qinhuangdao coastal areas in 2022.

To explore the causes of red tides in Qinhuangdao coastal water, we conducted surveys on both water quality and red tides during April to September of 2022 and analyzed the relationships between main environmental factors and red tide organisms through the factor analysis and canonical correspondence analysis. The results showed that there were eight red tides along the coast of Qinhuangdao in 2022, with a cumulative blooming area of 716.1 km2. The red tides could be divided into three kinds based on the major blooming organisms and occurrence time, Noctiluca scintillans bloom, diatom-euglena (Skeletonema costatum, Eutreptiella gymnastica, Pseudo-nitzschia spp.) bloom, and dinoflagellate (Scrippsiella trochoidea and Ceratium furca) bloom. Seasonal factor played roles mainly during July to September, while inorganic nutrients including nitrogen and phosphorus influenced the blooms mainly in April and July. The canonical correspondence analysis suggested that N. scintillans preferred low temperature, and often bloomed with high concentrations of ammonium nitrogen and dissolved inorganic phosphorus. S. costatum, E. gymnastica, and Pseudo-nitzschia spp. could tolerate broad ranges of various environmental factors, but favored high temperature and nitrogen-rich seawater. C. furca and S. trochoidea had higher survival rate and competitiveness in phosphate-poor waters. Combined the results from both analyses, we concluded that the causes for the three kinds of red tide processes in Qinhuangdao coastal areas in 2022 were different. Adequate diet algae and appropriate water temperature were important factors triggering and maintaining the N. scintillans bloom. Suitable temperature, salinity and eutrophication were the main reasons for the diatom-euglena bloom. The abundant nutrients and seawater disturbance promoted the germination of S. trochoidea cysts, while phosphorus limitation caused the blooming organism switched to C. furca and maintained the bloom hereafter.