MonkeyPox Cases in Karachi...............................

As of March 24, 2025, there have been reports of mpox (formerly known as monkeypox) cases in Karachi. In May 2024, a 36-year-old expatriate returning from Jeddah was diagnosed with mpox at Jinnah Postgraduate Medical Centre (JPMC) in Karachi. Earlier, in 2023, three passengers arriving at Karachi's Jinnah International Airport were diagnosed with mpox and admitted to the infectious disease hospital. 

Nationwide, since April 2023, Pakistan has reported at least 11 mpox cases, with one resulting in death. In August 2024, the Ministry of Health confirmed a case of mpox in a patient who had returned from a Gulf country, though the specific strain was not immediately identified. Given the evolving nature of the outbreak, it's advisable to consult local health authorities or official health department sources for the most current information on mpox cases in Karachi.                          

As of March 24, 2025, the global mpox (formerly known as monkeypox) situation has evolved significantly since the initial outbreak in May 2022. The emergence of new variants, such as clade 1b, has led to increased case numbers and fatalities in various regions. For instance, the Democratic Republic of the Congo has reported over 15,600 confirmed cases and 500 deaths associated with this strain. ​World Health Organization (WHO)+1Reuters+1Verywell Health+4Latest news & breaking headlines+4The Sun+4

For comprehensive and up-to-date graphical representations of worldwide mpox statistics, including case counts, geographical distribution, and mortality rates, you can refer to the following resources:​

• Our World in Data: This platform offers interactive charts and maps detailing the global spread of mpox, with data sourced from reputable health organizations. ​

                                    

• Gavi, the Vaccine Alliance: An article titled "Five charts on monkeypox, past and present" provides visual insights into the outbreak's progression and comparisons to historical data. ​

• World Health Organization (WHO): The WHO's mpox outbreak page includes situation reports and materials that often feature graphical data on case distribution and trends. ​World Health Organization (WHO).

                              

AI in Autism Research & Neurodevelopmental Disorders: Latest Developments

Artificial Intelligence (AI) is transforming autism research and the study of neurodevelopmental disorders. AI-driven models are enhancing early diagnosis, improving behavioral analysis, and aiding personalized interventions. Machine learning (ML) and deep learning (DL) are increasingly used to detect autism spectrum disorder (ASD), predict risk factors, and analyze behavioral patterns.

Recent Research on AI in Autism & Neurodevelopmental Disorders

1️⃣ AI for Early Autism Screening with Over 99% Accuracy

• Study: Deep learning models enhance autism diagnosis using behavioral and neuroimaging data.
• Findings: AI achieves high accuracy in detecting ASD at an early stage.
• 🔗 Read More (PDF)

2️⃣ AI in Emotion Recognition for Autism & Psychiatric Disorders

• Study: Examines how AI can assess cognitive impairments and emotional processing in neurodevelopmental disorders.
• Findings: AI models improve accuracy in emotion recognition for ASD patients.
• 🔗 Read More (PDF)

3️⃣ Machine Learning for ASD Risk Prediction

• Study: AI-based models identify ASD risk genes and predict neurodevelopmental outcomes.
• Findings: AI enables more precise identification of ASD genetic markers.
• 🔗 Read More (Springer)

4️⃣ AI-Powered Cry Analysis for Autism Detection

• Study: Uses AI to analyze infant cry patterns to predict ASD risk.
• Findings: Early detection through AI-based audio analysis improves intervention strategies.
• 🔗 Read More (Springer)

5️⃣ Gaming & AI for Neurodevelopmental Disorders

• Study: AI-integrated gaming enhances cognitive therapy for ASD patients.
• Findings: Reinforcement learning improves interaction and therapy outcomes.
• 🔗 Read More (Frontiers)

                                    

6️⃣ Comparing Pharmacological & Behavioral AI Interventions

• Study: AI evaluates the effectiveness of drug-based vs. behavioral treatments for ASD.
• Findings: Behavioral therapies show improved long-term outcomes with AI support.
• 🔗 Read More (PDF)

7️⃣ Multimodal Imaging & AI for ASD Diagnosis

• Study: MRI-based AI analysis detects neural abnormalities in ASD patients.
• Findings: AI enhances precision in diagnosing ASD through neuroimaging.
• 🔗 Read More (Journal of Psychological Science)

8️⃣ XGBoost-Based AI for Autism Prediction

• Study: AI models use genetic and behavioral data for ASD screening.
• Findings: XGBoost machine learning significantly improves ASD detection.
• 🔗 Read More (ScienceDirect)

9️⃣ AI & Gut Microbiome in Neurodevelopmental Disorders

• Study: AI analyzes gut microbiome differences in ASD and other neurodevelopmental disorders.
• Findings: Links found between gut health and ASD symptom severity.
• 🔗 Read More (MedRxiv)

🔟 AI for Nonverbal Autism: Audio-Based Diagnosis

• Study: AI processes nonverbal vocalizations to classify ASD severity.
• Findings: Improves ASD assessments for nonverbal individuals.
• 🔗 Read More (IEEE Xplore)

 

AI for Social Good: Emerging Research and Applications

Artificial Intelligence (AI) is increasingly being leveraged for social good, addressing global challenges across education, healthcare, environmental sustainability, and governance. Recent research explores how AI enhances social welfare, promotes ethical AI use, and mitigates societal inequalities.

Key Trends in AI for Social Good

• AI in Healthcare: Improving medical diagnoses, predicting disease outbreaks, and personalizing treatments.
• AI in Climate Change: Forecasting natural disasters, optimizing energy consumption, and advancing sustainable technologies.
• AI for Social Justice & Governance: Reducing bias in decision-making, improving transparency, and promoting digital human rights.
• AI for Education: Enhancing learning experiences and bridging educational gaps.
• AI in Economic Development: Supporting small businesses and optimizing resource allocation.

Recent Research on AI for Social Good

1️⃣ AI in Healthcare: Predicting Disease Risks in Older Adults

• Study: Examines how AI integrates social determinants and risk factors to predict cardiovascular diseases.
• Findings: AI-based risk models enhance early detection and preventive healthcare strategies.
• 🔗 Read More

2️⃣ AI in Climate Change Mitigation

• Study: Google Earth & AI are used for predicting climate events and sustainable resource management.
• Findings: AI models improve disaster response and environmental protection efforts.
• 🔗 Read More

3️⃣ AI for Governance: Protecting Digital Human Rights

• Study: AI’s role in shaping policies for human rights protection in the digital era.
• Findings: Calls for new AI governance frameworks to ensure fairness and accountability.
• 🔗 Read More

4️⃣ AI for Sustainable Urban Planning

• Study: AI-powered urban analytics for optimizing traffic, reducing pollution, and enhancing city planning.
• Findings: Machine learning aids in smart city developments by analyzing environmental impact.
• 🔗 Read More

5️⃣ AI for Social Justice: Reducing Bias in Decision-Making

• Study: AI applications in reducing discrimination in criminal justice and financial lending.
• Findings: Machine learning detects biases and ensures equitable access to opportunities.
• 🔗 Read More

6️⃣ AI for Education: Enhancing Personalized Learning

• Study: Examines how AI improves educational accessibility through adaptive learning tools.
• Findings: AI-based tutors provide personalized education, benefiting students with disabilities.
• 🔗 Read More

7️⃣ AI in Law & Governance: Managing Public Trust

• Study: AI’s impact on trust in digital governance and automated legal frameworks.
• Findings: Ethical AI implementation is critical for maintaining transparency in legal systems.
• 🔗 Read More

8️⃣ AI for Wildlife & Environmental Conservation

• Study: Machine learning models for tracking endangered species and protecting biodiversity.
• Findings: AI-driven monitoring systems help detect poaching and habitat destruction.
• 🔗 Read More

9️⃣ AI in Disaster Relief & Humanitarian Aid

• Study: AI-based predictive models for disaster management and humanitarian response.
• Findings: AI enables faster resource allocation in crisis zones and improves emergency response efficiency.
• 🔗 Read More

🔟 AI for Mental Health & Well-Being

• Study: AI models analyzing social media data to detect signs of mental health issues.
• Findings: Machine learning improves early intervention strategies for anxiety and depression.
• 🔗 Read More

 

 New Research Trends in AI

Emerging Research Trends in Artificial Intelligence (AI)

Artificial Intelligence (AI) continues to evolve rapidly, with new research trends shaping its future. Current advancements are focusing on AI's integration with various domains such as healthcare, education, and sustainable technologies. Key areas of interest include explainable AI (XAI), federated learning, AI ethics, neuro-symbolic AI, AI-driven automation, and generative models. Below are some of the most recent research contributions that highlight these developments:

1. AI in Healthcare: Detecting Patient-Ventilator Asynchrony

   • Study: AI-driven solutions for monitoring patient-ventilator interactions in ICUs.

   • Findings: Automated AI detection systems improve synchronization between patients and ventilators, reducing ICU complications.

2. AI for Education: Challenges & Opportunities

   • Study: Examines how AI impacts foundational knowledge in education.

   • Findings: AI-enhanced learning tools improve engagement but risk replacing deep critical thinking.

3. Blockchain & AI in Accounting

   • Study: AI and blockchain integration for secure financial transactions.

   • Findings: AI enhances fraud detection, and blockchain improves transparency.

4. AI in Public Health & Mental Wellness

   • Study: AI models tracking mental health trends via social media data.

   • Findings: Machine learning detects early signs of substance abuse and depression.

5. AI for Smart Manufacturing & Sustainability

   • Study: AI-enhanced Internet of Things (IoT) for sustainable manufacturing.

   • Findings: Optimized AI solutions reduce industrial waste and improve efficiency.

6. AI in Legal & Governance Systems

   • Study: Adapting legal frameworks to AI-driven transformations.

   • Findings: Legal policies must evolve with AI automation and data privacy concerns.

7. Deep Learning in Embedded Devices

   • Study: AI deployment on low-power embedded systems.

   • Findings: Optimized deep learning models can improve efficiency in IoT devices.

8. Neuro-Symbolic AI & Hybrid Models

   • Study: Combining deep learning with symbolic reasoning for explainable AI.

   • Findings: Improved interpretability of AI systems in critical decision-making applications.

9. AI in Drug Delivery & Medical Research

   • Study: AI's role in optimizing polymer nanoparticles for drug delivery.

   • Findings: AI accelerates drug formulation and precision medicine.

10.  AI in Autism Research & Neurodevelopmental Disorders

• Study: AI models for diagnosing and personalizing treatments for autism.

• Findings: Machine learning improves the detection of cognitive disorders in children.

 

Hanging Drop Technique (To Check Bacterial Motility)

SLIDE

MICROSCOPIC IMAGES 

MICROSCOPIC IMAGES 

 Gram-positive and Gram-negative Bacteria Images 

                                    

 

Spirochete Staining Images using the India-Ink Method

                        

                        

                        

 

Acid-Fast Staining Images 

 

Bacterial Endospore Staining 

 Capsule Staining Images 

 Cell Wall Staining Images

 

New Strains of Coronavirus

 As of March 16, 2025, several new strains and variants of coronaviruses have been identified, prompting attention from the global health community.

SARS-CoV-2 Variants:

• BA.2.86 and JN.1: The Omicron subvariant BA.2.86, first detected in July 2023, has accumulated over thirty mutations on its spike protein compared to its predecessor, BA.2. Its descendant, JN.1 (also referred to as "Pirola"), emerged in August 2023 and became the dominant strain during the winter of 2023–2024. The World Health Organization (WHO) designated JN.1 as a variant of interest in December 2023, noting its widespread prevalence across multiple regions. 

Novel Coronavirus Discoveries:

• HKU5-CoV-2: Researchers in China have identified a new bat coronavirus named HKU5-CoV-2. This virus can utilize the human ACE2 receptor for cell entry, similar to SARS-CoV-2, suggesting potential for cross-species transmission. While no human infections have been reported to date, its genetic similarity to the Middle East respiratory syndrome (MERS) virus—which has a higher mortality rate—raises concerns. Ongoing surveillance and stringent laboratory safety protocols are recommended to monitor and mitigate any potential risks associated with this virus. Continued vigilance and research are essential to understand the implications of these new strains and to develop appropriate public health responses.

Reference links:

https://www.news.com.au/world/asia/chinese-researchers-discover-new-bat-coronavirus/news-story/43e6cbf5c68c0c7e82bedc9b8ed15b7e?utm_source=chatgpt.com

https://www.thescottishsun.co.uk/health/14400894/coronavirus-pandemic-fears-bat-bug-new-virus/?utm_source=chatgpt.com

https://www.reuters.com/business/healthcare-pharmaceuticals/chinese-researchers-find-bat-virus-enters-human-cells-via-same-pathway-covid-2025-02-21/?utm_source=chatgpt.com

The newly identified HKU5-CoV-2 is a bat coronavirus closely related to MERS-CoV. While its exact structure hasn't been fully published, it is expected to share key features with other coronaviruses:

1. Spherical or pleomorphic viral envelope
2. Spike (S) protein – responsible for binding to human ACE2 receptors
   
3. Membrane (M) and Envelope (E) proteins – crucial for viral assembly
4. Nucleocapsid (N) protein – protects the viral RNA genome

                    

below the structure comparison with SARS-CoV-2, BA.2.86, JN.1

Feature	SARS-CoV-2 (Original Strain)	BA.2.86 (Pirola Variant)	JN.1 Variant
Structure	Spherical with uniform spike proteins	Spherical with altered spike proteins	Similar to BA.2.86 but with refined spike mutations
Spike (S) Protein	Fewer mutations, allowing strong ACE2 binding	Over 30 mutations in the spike protein	Contains additional L455S mutation
Mutation Impact	Basic transmission, lower immune evasion	Increased immune evasion but slightly reduced receptor binding	Higher immune escape, better adaptation
Transmissibility	Moderate	High due to immune evasion	Very high, dominant in 2024
Immune Evasion	Lower; vaccines provide strong protection	Higher, raising concerns about vaccine escape	Even higher, making reinfections more common
Dominance Period	2020	Late 2023	Late 2023 – Early 2024

 https://micro-bites.org/

 AI in the microbiome

Research on the microbiome benefits today from artificial intelligence through its capability to run sophisticated data analyses and conduct predictive computations. AI brings its most valuable support to microbiome sequencing alongside interpretation of the generated data. The large-scale complex data created by metagenomic sequencing ceases the identification of microbial species effectively. The use of artificial intelligence algorithms with machine learning (ML) technology allows for quick bacterial community classification and simultaneous detection of new microbial strains and forecasting of their ecological functional behavior. The automation strategy cuts down both time requirements and human work needed for microbiome research, which allows scientists to study microbial patterns at previously unreachable levels.

AI uses advanced techniques to connect microbial communities with diseases by finding relationships between these microbiomes and different illnesses. AI models use microbiota composition data to reveal disease predictions along with information about microbiome conditions affecting inflammatory bowel disease (IBD), obesity, and diabetes. The field of personalized medicine utilizes AI-driven approaches to recommend customized dietary and probiotic treatments that match the specific microbiome profiles of each patient. The recent discoveries enable medical professionals to develop personalized healthcare methods that enhance microbiome stability alongside general health outcomes. 

The usage of AI technology in microbiome research aims to forecast antibiotic resistance patterns. The worldwide increase in antimicrobial resistance has created a health emergency that AI systems can overcome by detecting resistance elements in microbial populations. Modern AI systems analyze genome data to recognize resistance patterns, which helps researchers create new antibiotics together with alternative medical plans. Early identification of resistant strains remains vital in clinical microbiology since it allows physicians to enhance patient care together with proper treatment planning.

Overall, AI is revolutionizing microbiome research by accelerating discoveries, improving diagnostic capabilities, and enhancing our understanding of microbial communities. As AI continues to evolve, its integration with microbiome studies will lead to more precise diagnostics, innovative therapies, and a deeper understanding of the intricate relationships between microbes and their environments.

Artificial intelligence (AI) has significantly advanced microbiome research, leading to the development of several tools that enhance data analysis and clinical applications. Here are some notable AI-driven tools in microbiome analysis:

 

1. MEGAN (Metagenome Analyzer): MEGAN is a bioinformatics tool designed for analyzing large-scale metagenomic datasets. It processes DNA sequences from environmental samples, compares them against reference databases, and assigns taxonomic classifications using algorithms like the Lowest Common Ancestor (LCA). MEGAN has been instrumental in studies ranging from ancient DNA analysis to marine biology.

2. PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States):

PICRUSt predicts the functional composition of a microbial community using marker gene data, such as 16S rRNA sequences. By leveraging known genomic information, it infers the presence of specific gene families within a sample, aiding in understanding the functional potential of microbial communities.

3. QIIME (Quantitative Insights into Microbial Ecology):

QIIME is a comprehensive platform for analyzing high-throughput microbiome sequencing data. It supports various data types, including marker gene sequences and metagenomic data, facilitating tasks like quality filtering, taxonomic assignment, and diversity analyses. QIIME has evolved into a multi-omics platform, integrating various data types to provide a holistic view of microbial communities. 

4. Machine Learning Applications in Metagenomics
Machine learning techniques are increasingly applied in metagenomics to classify microbial communities and predict disease associations. For instance, algorithms like random forests and neural networks have been utilized to distinguish between healthy individuals and those with conditions such as inflammatory bowel disease (IBD) or colorectal cancer. These models analyze complex microbial patterns, contributing to improved diagnostics and personalized medicine approaches. These AI-driven tools and methodologies have revolutionized microbiome research, enabling more accurate analyses and fostering advancements in clinical applications.