Proper Test Research Articles

Pyridoxal phosphate binding protein (PLPBP) deficiency mimicking opsoclonus‐myoclonus‐ataxia syndrome

AbstractIntroductionGenetic and metabolic conditions can mimic diagnoses such as hypoxic‐ischemic encephalopathy, meningoencephalitis, epilepsy, and opsoclonus‐myoclonus‐ataxia syndrome (OMAS). Without a high index of suspicion and proper testing, diagnoses can be missed, and treatment delayed.MethodsA 3‐year‐old girl with a history of neonatal seizures and previous nondiagnostic epilepsy gene panel presented with seizures, behavioral changes, and discrete episodes of myoclonus, tremors, and abnormal eye movements following a viral illness.Objective and InterpretationInitial evaluation was concerning for OMAS, though metabolic causes remained on the differential. Metabolic testing revealed elevated glycine and glutamine, suggestive of a possible inborn error of metabolism. Whole exome sequencing demonstrated compound heterozygous variants in the PLPBP gene associated with pyridoxine‐dependent epilepsy (PDE), consistent with her clinical presentation and leading to her diagnosis of PLPBP deficiency.DiscussionClinicians should gauge the indications, advantages, and limitations of targeted sequencing panels versus whole exome sequencing. Continued evaluation is recommended in patients with a history of neonatal and infantile epilepsy, especially if they present with episodic crises related to viral illness even if prior genetic and metabolic investigations have been nondiagnostic. This report also highlights the clinical overlap between PLPBP deficiency and OMAS, and the differences in pathophysiology, treatment pathways, and implications.

Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.

Empowering Nurses for Effective Diagnostic Stewardship: An Initiative to Address Anti-Microbial Resistance

AimThis study aimed to evaluate nurses' baseline understanding of diagnostic stewardship (DS) principles and the impact of an educational intervention on their knowledge and practices. BackgroundAntimicrobial resistance (AMR), driven by inappropriate antibiotic use, is a critical global health threat. Effective antimicrobial stewardship (AMS) requires integrating DS to ensure accurate diagnoses through proper test requisition, specimen handling and timely reporting. However, understanding and implementation of DS remains poor in general, contributing to inappropriate antibiotic use. Nurses play a crucial role in DS, yet their potential is underused, highlighting the need for targeted educational interventions. DesignA quasi-experimental pre-post study conducted from September 2022 to March 2023. MethodsA gap analysis assessed nurses' baseline knowledge of DS informing the development of an online training program. In the intervention phase, pre-and post-assessments measured knowledge improvements after the intervention. ResultsIn phase 1, 310 out of 517 nurses completed the gap analysis, with an average score of 6.59 out of 20 (range: 2-13; median score: 6.5). In phase 2, 228 of 613 nurses completed both pre- and post-tests, showing a significant improvement in scores from 4.69 (median 5), in pre-test to 6.22 (median 6) in post-test. ConclusionThe educational intervention significantly enhanced nurses' knowledge of AMR, specimen selection, culture indications and collection techniques. Sustained training is vital to strengthen diagnostic practices, reduce inappropriate antimicrobial use and combat AMR.

Mpox (Monkeypox) On Board a Naval Warship.

We report a case of mpox in an active duty male on board a naval warship, who presented with a chancre-like penile lesion and ulcerating lymphadenopathy in the setting of a nonreactive treponemal test. Despite empirical therapy for sexually transmitted infections, he developed a fever and a generalized pustular rash. The patient was placed in isolation, and contact tracing was performed. No further spread of mpox was identified despite close contact with multiple shipmates and a pet rabbit. This case highlights the obligation of medical providers who care for our military personnel and veterans to offer JYNNEOS vaccine to high-risk individuals. Additionally, with the rise in clade I mpox cases prompting a recent global health emergency declaration by the World Health Organization, it is essential to implement proper testing methods and treatments when considering mpox in the differential diagnosis because of its association with other sexually transmitted infections.

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters.

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention. This study developed an ML model to identify HDP risk before clinical onset using affordable methods. Features were extracted from blood pressure (BP) measurements, body mass index values (BMI) recorded during the first and second trimesters, and maternal demographic information. We employed a random forest classification model for its robustness and ability to handle complex datasets. Our dataset, gathered from large academic medical centers in Atlanta, Georgia, United States (2010-2022), comprised 1,190 patients with 1,216 records collected during the first and second trimesters. Despite the limited number of features, the model's performance demonstrated a strong ability to accurately predict HDPs. The model achieved an F1-score, accuracy, positive predictive value, and area under the receiver-operating characteristic curve of 0.76, 0.72, 0.75, and 0.78, respectively. In conclusion, the model was shown to be effective in capturing the relevant patterns in the feature set necessary for predicting HDPs. Moreover, it can be implemented using simple devices, such as BP monitors and weight scales, providing a practical solution for early HDPs prediction in low-resource settings with proper testing and validation. By improving the early detection of HDPs, this approach can potentially help with the management of adverse pregnancy outcomes.

Commissioning and implementation of a pencil-beam algorithm with a Lorentz correction as a secondary dose calculation algorithm for an Elekta Unity 1.5T MR linear accelerator.

To commission a beam model in ClearCalc (Radformation Inc.) for use as a secondary dose calculation algorithm and to implement its use into an adaptive workflow for an MR-linear accelerator. A beam model was developed using commissioning data for an Elekta Unity MR-linear accelerator and entered into ClearCalc. The beam model consisted of absolute dose calculation settings, output factors, percent depth-dose (PDD) curves, mutli-leaf collimator (MLC) transmission and dose leaf gap error, and cryostat corrections. Beam profiles were hard-coded by the manufacturer into the beam model and were compared with Monaco-derived profiles. The beam model was tested by comparing point doses in a homogenous phantom obtained through measurements using an ionization chamber in water, Monaco, and ClearCalc for various field sizes, source-surface distances (SSDs), and point locations. Additional testing including point dose verification for test plans using a heterogeneous phantom and patient plans. Post clinical implementation, performance of ClearCalc was evaluated for the first 41 patients treated, which included 215 adaptive plans. PDDs generated using ClearCalc fell within 1.2% of measurements. Field profile comparison between ClearCalc and Monaco showed an average pass rate of 98% using a 3%/3mm gamma criteria. Measured cryostat corrections used in the beam model showed a maximum deviation from unity of 1.4%. Point dose and field monitor units (MUs) comparisons in a homogenous phantom (N=22), heterogeneous phantoms (N=22), and patient plans (N=57) all passed with a threshold of 5%/5MU. Clinically, ClearCalc was implemented as a physics check post adaptive planning completed prior to beam delivery. Point dose and field MUs showed good agreement at a 5%/5MU threshold for prostate stereotactic body radiation therapy (SBRT), pelvic lymph nodes, rectum, and prostate and lymph node plans. This work demonstrated commissioning and clinical implementation of ClearCalc into an adaptive planning workflow. No primary or adaptive plan failures were reported with proper beam model testing.

Direct assessment of hereditary hemochromatosis in preimplantation genetic testing.

Hereditary hemochromatosis (HH) is a common genetic disorder characterized by iron overload, which, if undiagnosed, can lead to severe organ damage. There are 4 types of HH. Type 1 HH, the most common form, is primarily caused by a common variant in Western Europe (p.Cys282Tyr, C282Y, or c.845 G>A). It is generally preventable during invitro fertilization if proper genetic testing is performed before implantation. Here, we demonstrated a direct detection and cost-effective approach using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in preimplantation genetic testing (PGT) settings. We began by validating the assay with genomic deoxyribonucleic acid (DNA) from Coriell cell lines of known HFE C282Y genotypes, followed by testing patients' genomic DNA samples. After establishing the assay on genomic DNA, we extended the assay to whole-genome amplified DNA from embryo biopsies. The subjects include cell line samples and human specimens and human embryo biopsies. Patients and embryos either carried or did not carry the HFE C282Y variant in their genome. No intervention was applied. The readout included the genotype of samples at the HFE C282Y locus and accuracy of PCR-RFLP results. An accuracy of >99% was achieved across 80 cell line samples, 38 patient samples, and 81 embryo biopsies. In this study, we demonstrated the feasibility of using the PCR-RFLP approach to PGT. Specifically, we validated the assay for the HFE C282Y variant, the primary cause of type 1 hemochromatosis. The assay was tested on genomic DNA and DNA resulting from whole-genome amplification, achieving >99% accuracy, sensitivity, precision, and specificity. These results also suggest the possibility for extending the PCR-RFLP approach to cover a broader range of conditions, such as spinal muscular atrophy, to benefit more patients currently ineligible for testing at PGT laboratories.

Menilai Kualitas Pemimpin: Fit and Proper Test dalam Demokrasi Berbasis Moralitas, Intelektualitas, dan Elektabilitas

Introduction: The Fit and Proper Test (FPT) is an important process in assessing potential leaders in the democratic country of Indonesia, which aims to ensure that candidates meet the requirements of morality, intellectuality and electability. Based on Law no. 17 of 2014 and Law no. 8 of 2015, the morality aspect requires prospective leaders to have integrity and a good track record. However, challenges arise from politicization which often ignores these values.Purposes of the Research: The aim of this research is to analyze the effectiveness of the Fit and Proper Test in the context of morality, intellectuality and electability, as well as provide recommendations for improving the leader selection process in Indonesia.Methods of the Research: This research uses a qualitative approach with descriptive analysis, collecting data through literature studies and expert interviews. The results are expected to provide an overview of the effectiveness of the fit and proper test regarding morality, intellectuality and electability, as well as recommendations for future improvements. Historical and conceptual approaches are also applied to systematically understand legal philosophy.Results of the Research: The conclusion of this research confirms that the Fit and Proper Test (FPT) has a vital role in selecting quality leaders in Indonesia. This process must consider morality, intellectuality and electability in accordance with the provisions of the law. Although challenges in implementation often arise due to politicization, transparent and accountable FPT reform is very important to produce leaders who are able to advance the nation.

Siblings with nemaline myopathy: a case of rare genetic mutation

Nemaline myopathy (NM) is a rare, congenital myopathy with varied clinical presentation. This case report talks about varied manifestation of this condition among siblings from same couple with a rare specific mutation at molecular level among the known mutations reported for NM. The subsequently mentioned patient had severe respiratory distress and recurrent respiratory tract infections since infant, which led to meticulous investigations and relevant family history charting. Such methods proved to be fruitful for suspecting and subsequently led to successful diagnosis of NM in the patient and aforementioned siblings. The diagnosis of NM was made with proper genetic testing, and patient was put on ventilatory support and appropriate physiotherapy for muscle weakness. Lower respiratory tract infections were treated with appropriate antibiotics and medications as and when required. To conclude this case report, advising adequate pre-conceptional genetic counselling to the couple having affected siblings with dire outcomes, which helps avoid the psychological, financial and medical demands of managing such conditions.

Social-sum-Mal: A Dataset for Abstractive Text Summarization in Malayalam

Abstractive text summarization techniques for Malayalam language is still in its infancy. The lack of benchmarked datasets for this task is one of the constraints in developing and testing good models. Malayalam has seven nominal case forms, two nominal number forms, and three gender forms. It is subjected to extreme agglutination and inflection. Due to this, the translation of other text summarization datasets to Malayalam may not capture these case forms effectively. Therefore curation of datasets from scratch is highly demanded for specific text-processing applications in Malayalam. This article introduces a novel dataset designed specifically for advancing the field of automatic abstractive text summarization in Malayalam language. The dataset is curated to address the unique linguistic characteristics of the Malayalam language. It is called the Social-sum-Mal dataset , capable of addressing three different types of summarization tasks: long, extreme, and query-based summarizations. In addition, Social-sum-Mal can be extended for other applications like text classification, multi-document summarization, and question answering. To enhance the dataset transparency, a datasheet is created for Social-sum-Mal. Data accuracy and annotator biases are evaluated using proper testing strategies including Jaccard, cosine, and overlap similarities. The correctness of the dataset is further evaluated by comparing it with a deep learning based text summarization model.

Influence of Gut Microbiota on Drug Metabolism and Therapeutic Efficacy

Background: The natural complex of microorganisms that inhabit the digestive system is estimated to be trillions of bacteria and is known as the gut microbiota. It is now being appreciated more for its involvement in metabolism of drugs, and effects exerted on efficacy and toxicity of drugs. Differences in the composition of the microbiota in the gut cause differences in drug response among people, and therapeutic processes.Objectives: To evaluate the modulation of gut microbiota on drug metabolism and outcome, and analyze the therapeutic outcomes influenced by the microbiome, and to also identify microbial indicators predictive of variable therapeutic outcomes.Study Design: A Cross-Sectional Study.Place and Duration of the Study: Department of Biochemistry, Saidu Medical College, Swat KP – Pakistan from January 2022 to January 2023.Methodology: The current study recruited a sample of 150 patients. The bacterial structure in the gut was investigated using 16S rRNA analysis, and drug metabolism was determined through the blood plasma samples taken at the required time points. Patients were categorized according to their drug efficacy performance, and statistical comparisons were made to evaluate the relationships, accompanied by standard deviations and p-values, respectively, for proper testing of the results’ significanceResults: Among the 150 patients, a higher number of bacterial strains was also associated with better drug outcomes in terms of SD (.35, p < 0.05). The comparison of the rates of metabolism of the drugs between both groups revealed a group difference (SD = 0.28, p = 0.01). They found that those patients who had longer and more complex bacterial signatures had better outcomes from their treatment than patient with less diverse levels of bacteria in their gut.Conclusion: The buy-in of the study is that a direct association between the gut microbiota profile and Drug metabolism efficiency and effectiveness exists. It may also help in identifying biomarkers of microbial origin that may help to improve treatment outcomes, improve compliance with individual treatment plans, and reduce side effects.

Model-Agnostic Adaptive Testing for Intelligent Education Systems via Meta-learned Gradient Embeddings

The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a fundamental challenge is designing proper test for assessing the students’ cognitive status on knowledge and skills accurately and efficiently. One promising approach, referred to as Computerized Adaptive Testing (CAT), is to administrate computer-automated tests that alternately select the next item for each examinee and estimate their cognitive states given their responses to the selected items. Nevertheless, existing CAT systems suffer from inflexibility in item selection and ineffectiveness in cognitive state estimation, respectively. In this article, we propose a Model-Agnostic adaptive testing framework via Meta-leaned Gradient Embeddings, MAMGE for short, improving both item selection and cognitive state estimation simultaneously. For item selection, we design a Gradient Embedding-based Item Selector (GEIS) which incorporates the concept of gradient embeddings to represent items and selects the best ones that are both informative and representative. For cognitive state estimation, we propose a Meta-learned Cognitive State Estimator (MCSE) to automatically control the estimation process by learning to learn a proper initialization and dynamically inferred updates. Both MCSE and GEIS are inherently model-agnostic, and the two modules have an ingenious connection via meta-learned gradient embeddings. Finally, extensive experiments evaluate the effectiveness and flexibility of MAMGE.

The Development of Interactive Learning Media for Islamic Religious Education in Elementary Schools in Indonesia

In today’s digital era, conventional teaching methods often fail to captivate students, making interactive media a promising solution. Interactive media combines visual, audio, and participatory elements, providing a more engaging and effective learning experience. The development process includes analyzing student needs and curriculum, selecting appropriate technology, creating interactive content, conducting trials, and evaluating effectiveness. This study uses a qualitative approach. Primary data sources in qualitative research are the words of the interview results obtained from primary data including respondents from teachers and students in SDMT Ponorogo. Data collection is carried out by means of snowball sampling. The data collection technique in this study is through observation, in-depth interviews and documentation. The data obtained during the study, both primary and secondary data are then processed and analyzed using Miles and Huberman model. The validity test of data in qualitative research includes tests, credibility, transferability, dependability, and confirmability. The article highlights the benefits of interactive media, including increased student motivation, improved understanding of abstract religious concepts, and the development of critical thinking skills. This approach not only modernizes religious education but also ensures that students remain interested and actively involved in learning. Through proper testing and implementation, interactive media can significantly enhance the quality of Islamic Religious Education at SDMT Ponorogo. The developed media, which consists of animated videos, interactive quizzes, and educational games, is able to make the learning process more engaging and increase student engagement.

The role of alignment for valid tensile testing of ceramic matrix composites

The determination of tensile properties of ceramic matrix composites (CMCs) is crucial for material development and assessment. Despite the damage tolerance of CMCs, the efforts required for proper tensile testing are similar high than for ceramics. This study describes a method to determine the sample alignment prior every single tensile testing and discusses the required amount of alignment for valid testing. For this purpose, carbon fiber-reinforced silicon carbon (C/C-SiC) samples were evaluated by only two alignment criteria. Approximately 80 % of the tensile samples failed valid with a percentage bending less than 6 % and no valid testing was achieved at a percentage bending higher than 12.3 %. Invalid testing showed an increase of strength scattering of more than 50 %, but only a minor change of the strength. The preliminary alignment check and subsequent realignment was able to increase the rate of validly tested samples by about four times.

Profile Laboratory of Thalassemia in the General Hospital Dr. Zainoel Abidin Banda Aceh

Thalassemia refers to the globin gene abnormality that affects the a and b globin genes. A comprehensive peripheral blood laboratory examination, Mentzer index, blood morphology, and Hb analysis were all performed on every patient with thalassemia who attended treatment as part of this analytical observational study. This research aims to know the profile of laboratory examinations of thalassemia patients in the Dr. Zainoel Abidin General Hospital (RSUZA) Banda Aceh. The study's target population was 547 individuals, with 278 males and 269 females making up the male and female split, respectively (50.7% and 49.3%). With a total of 197 subjects (36.4%), this study revealed that the subjects were most frequently found in the age range of 0–5 years. Only 8 subjects, or 1.5%, of the population in the over-65 age group, were detected. The typical value of the erythrocyte index further fell and was discovered in 469 participants with a value of 85.3%. A total of 504 patients, or 91.6%, had more elevated RDW features. Results for the Mentzer index with 432 subjects were determined to be 13 were 78.5%. In the 450-subject hypochromic microcytic group, subjects were most frequently observed (81.8%). Blood morphology features from 505 participants, or 91.8% of the total, showed no additional blood morphology characteristics in normoblasts, and from 361 subjects, or 65.6% of the total, showed no target cells. When a person has anemia or a family history of thalassemia, it is strongly advised that they get a full blood count (CBC). The proper laboratory testing is still necessary for the health issue of thalassemia.

Correlation Between Level of Physical Activity Using GPAQ and Exercise Capacity Using 6 Min Walk Test in Non-Insulin Dependent Type 2 Diabetes Mellitus

Background: This study was designed to find the Correlation Between the Level of Physical Activity and Exercise Capacity in Non-Insulin Dependent Type 2 Diabetes Mellitus. Purpose: To find the Correlation between the Level of Physical Activity and Exercise Capacity in Non-Insulin Dependent Type 2 Diabetes Mellitus. Materials and Methods: Materials required for this study were Countdown timer (or stopwatch), Two small cones, Chair, Worksheet on a clipboard, Sphygmomanometer. It was a correlational study design. The sampling method used was Convenient type of sampling. Sixty subjects with non-insulin dependent type 2 were included in this study. The written informed consent was taken. Informed consent document was signed, retained by the principal investigator and a copy was given to the participant. Global Physical Activity Questionnaire and 6 Minutes walk test was used to study the Physical Activity and Exercise Capacity respectively. All the data obtained was statistically analysed using proper tests. Results: The results showed positive significant (p value < 0.0001) correlation between the level of physical activity and exercise capacity, Physical activity level correlates statistically with distance walked in percentage. Conclusion: Our study concluded that Physical activity level correlate statistically with distance walked in type 2 diabetic patients.

A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks

Purpose: Adverse Drug Reactions (ADR) classification is useful in modern medical diagnostics and related applications. ADR is an example of how medical information is frequently accessible on social media platforms for healthcare, where people can share their experiences with treatments on desktop computers and mobile devices. Many researchers are interested in gathering valuable medical data from social media for the ADR system training and classification process. Materials and Methods: This research explores the effects of three aspects on recognizing ADR mentions in social media for the medical field and proposes a deep neural network of Long Short-Term Memory (LSTM) neural networks to do so. The comments are collected from various social media platforms to implement the ADR system with proper training and testing processes. The texts from the dataset are initially preprocessed by using a data filtering and clustering process to remove the input data's redundant information to increase the training process's quality. Characteristic features, such as semantic features and text statistics, are extracted from the input text using the American Standard Code for Information Interchange (ASCII) array. Further, the features are converted and fed to LSTM networks for training and validation. Results and Conclusion: This work is evaluated using two datasets, CODEC, and ADR Corpus datasets are used to evaluate the performance of the proposed ADR technique via multiple angles. Via extensive experiments, this work achieved 99.79 accuracy, 98.37 sensitivity, 97.63 specificity, 99.72 precision, 98.39 recall, 97.62 F1-score for the CODEC dataset, 98.16 for accuracy, 99.19 for sensitivity, 98.49 for specificity, 99.49 for precision, 96.72 for recall, and 93.16 for F1-score for ADR corpus, respectively.

Validation of Saliva as the Clinical Specimen Type for a University-Wide COVID-19 Surveillance Program.

At the beginning of the COVID-19 pandemic, the Georgia Institute of Technology made the decision to keep the university doors open for on-campus attendance. To manage COVID-19 infection rates, internal resources were applied to develop and implement a mass asymptomatic surveillance program. The objective was to identify infections early for proper follow-on verification testing, contact tracing, and quarantine/isolation as needed. Program success depended on frequent and voluntary sample collection from over 40,000 students, faculty, and staff personnel. At that time, the nasopharyngeal (NP) swab, not saliva, was the main accepted sample type for COVID-19 testing. However, due to collection discomfort and the inability to be self-collected, the NP swab was not feasible for voluntary and frequent self-collection. Therefore, saliva was selected as the clinical sample type and validated. A saliva collection kit and a sample processing and analysis workflow were developed. The results of a clinical sample-type comparison study between co-collected and matched NP swabs and saliva samples showed 96.7% positive agreement and 100% negative agreement. During the Fall 2020 and Spring 2021 semesters, 319,988 samples were collected and tested. The program resulted in maintaining a low overall mean positivity rate of 0.78% and 0.54% for the Fall 2020 and Spring 2021 semesters, respectively. For this high-throughput asymptomatic COVID-19 screening application, saliva was an exceptionally good sample type.

A GMO Experiment on Two-Thirds of the World’s Population: Reaction to Ulrich’s Commentary on Lee and Broudy (2024)

While Professor A. Ulrich (2024) raised justifiable questions about the interpretation of microscopic images from incubated COVID-19 injectables from Pfizer and Moderna, her message treats the world-wide genetic modification experiment with COVID-19 injectables on humans — an experiment on us without our consent — as normal. I will not discuss the physico-chemistry of the 10 trillion lipid nanoparticles she claims are in every dose of the injectables. She acknowledges that “many of the lipid constituents . . . tend to induce inflammation” (p. 1244.3) but says they account for all the self-assembling structures documented in what she describes as the “reliable” and “fully consistent” findings (Ulrich, p. 1244.7) of Lee and Broudy (2024). Though I believe the self-assembling structures also merit in-depth study, the technology underlying them, must be contained in the concoctions injected into the arms of two-thirds of the world’s population without proper preclinical testing and without the informed consent required by the Nuremberg Code. My focus is on that violation of medical ethics turning humans into genetically modified organisms. Here I illustrate in her words that such a genetic experiment on humans is underway. Instead of refuting any of the evidence in Lee and Broudy (2024) —that whatever the injectables may actually contain they are harming the human population of the world — she actually admits in closing that those “risks will be exacerbated with the next generation of self-amplifying or self-replicating RNA vectors for vaccines” (1244.8).

Translation, Cultural Adaptation, and Psychometric Properties of Hindi Rosenberg Self-esteem Scale in University Nursing Students.

Rosenberg's Self-esteem Scale (RSES) has been widely used without proper translation, cultural adaptation, and testing psychometric properties in Indian studies. The study aimed to translate and culturally adapt the 10-item Rosenberg Self-esteem Scale into Hindi and find psychometric properties. We have translated the scale using World Health Organization (WHO) guidelines. Using a cross-sectional design, the translated scale was administered to 143 undergraduate nursing students randomly selected. Content validity index (CVI) was used to assess content validity. Exploratory factor analysis (EFA) with principal axis factoring was used to estimate the construct validity. Cronbach's alpha (α) and intraclass correlation coefficient (ICC) were used to check internal consistency and test re-test reliability. The S-CVI for the Hindi RSES was 0.98. Kaiser-Meyer-Olkin (KMO) (=0.79), and Bartlette's test for sphericity was significant (χ 2 = 425.06, df = 45, P < .001). EFA extracted three common factors, explaining 62.62% of the total variance. The factor loading of all items suggests a good representation of their underlying construct; all items >0.47, except item 1 (= -0.373). The test re-test, an ICC was r = 0.76 (P < .001), and Cronbach's alpha (α) after deletion of item 1 was 0.81, suggesting excellent reliability. The Hindi Rosenberg self-esteem scale is a psychometrically tested scale to assess self-esteem in the Hindi-speaking adult population. These findings provide empirical judgment for using the translated Hindi RSES to measure self-esteem among the adult population. The authors recommend verifying the psychometric properties in a more extensive and varied population.