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Multiomic ALS Study Links Peripheral Immune Infiltration to CNS Inflammation

A new study from scientists at Northwestern University Feinberg School of Medicine sheds light on how amyotrophic lateral sclerosis (ALS) unfolds in the body. Specifically, they found that the disease proceeds through a “domino-like” sequence of events that begins with an early breakdown inside motor neurons that is followed by a damaging inflammatory response. Insights from this study could help explain why the disease worsens over time, why some patients progress faster than others, and how future treatments could be more personalized. Details of the work are available in a new Nature Neuroscience paper titled “Integrated single-cell and spatial transcriptomic profiling in ALS uncovers peripheral-to-central immune infiltration and reprogramming.”

On average, patients with ALS live three years after symptoms begin, although some can survive closer to 10 years. Exactly what drives these differences in survival is unclear. “This study reveals that ALS is not a single event but a domino-like cascade that begins inside motor neurons with TDP-43 pathology and is then amplified by a damaging immune response in the bloodstream and spinal cord,” said David Gate, PhD, director of the Abrams Research Center on Neurogenomics at Feinberg and co-corresponding author on the study. 

Specifically, the study found that immune cells converge at sites of motor neuron loss and TDP-43 pathology with distinct inflammatory patterns depending on the type of ALS and how quickly the disease progresses. As Evangelos Kiskinis, PhD, an associate professor of neurology and neuroscience at Feinberg and a co-corresponding author on the study, explained it, “the intensity of spinal cord inflammation” determines “how fast the disease progresses and how long they survive.” 

To gain these insights, the scientists analyzed blood and spinal cord samples from living and deceased patients with both genetic and non-genetic forms of ALS, as well as controls. As part of the study, they used single-cell RNA sequencing technology to analyze blood from 40 living ALS patients and used spatial transcriptomics to analyze spinal cord tissue from 18 deceased participants. They also compared patients with non-genetic ALS to those with the genetic form of the disease to assess how immune activity differs across ALS types and disease stages. Lastly, they examined RNA from postmortem samples of 237 ALS patients to better understand the inflammatory responses within the central nervous system. 

Using these methods, “we found the immune cells we detected in the blood of people living with ALS were inflamed, and we found the genes that mediate their inflammatory response in the spinal cord at the site of motor neurons,” Gate said. “These inflamed immune cells were associated with ALS pathology, giving some credence to our theory that the immune system is detrimental. It’s responding to pathology, and it’s causing the disease to be worse.”

Additionally, patients whose disease advanced quickly had more activity in certain immune genes, while those with the genetic form of the disease had a different set of altered immune genes. In the spinal cord, these activated immune cells gathered directly at the locations of motor neuron loss and near the toxic protein buildups associated with ALS. “We saw that people with worse clinical ALS had more expression of complement genes, which are proteins that become activated as the body’s first-line immune defense against a pathogen or damage to the body,” Gate said.

Now that they have identified a direct link between the immune system and ALS, Gate and his lab plan to study samples from a wider pool of patients. “Our next step is to map exactly how this immune reaction spreads throughout the entire motor circuit: from the brain, down through the spinal cord and out to the muscles,” he said. “By profiling the motor circuit in depth, we’ll get a much clearer picture of where and when inflammation drives faster progression.” 

Meanwhile, Kiskinis and his team will test for a causal relationship between TDP-43 dysfunction and inflammation. “We’re trying to really define what is the mechanism that links TDP-43 dysfunction in nerve cells with inflammatory reactions,” he said.

The post Multiomic ALS Study Links Peripheral Immune Infiltration to CNS Inflammation appeared first on GEN – Genetic Engineering and Biotechnology News.

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PPG-Derived Digital Biomarker Developed for Peripheral Artery Disease Detection

A research team at the University of California, San Diego has developed a machine learning-based screening approach for peripheral artery disease (PAD) that uses a light-based technology called photoplethysmography (PPG) that can measure changes in blood volume in tissue. The researchers reported that short-duration PPG recordings in a patient’s toe, analyzed by machine learning models, identified PAD with a high degree of accuracy and may provide the basis for a scalable digital screening tool that could eventually be deployed through smartphones, pulse oximeters, and wearable devices. The team’s findings are published in npj Digital Medicine.

“PPG works by shining a light into tissue, in our case, the toe,” said co-first author Ava J. Fascetti, a PhD student in the digital health technology lab of senior author Edward J. Wang, PhD. “A photosensor measures how much light is reflected back, allowing us to detect tiny changes in blood volume: what we call the PPG signal.”

PAD is caused by plaque buildup in arteries that restricts blood flow, particularly to the legs and lower extremities. The disease affects an estimated 12 million Americans and 200 million adults worldwide. PAD substantially increases the risk of limb loss and major cardiovascular events, yet many patients are not diagnosed until later stages of disease progression. The researchers noted that the condition disproportionately affects underserved populations and is underdiagnosed in part because the current standard diagnostic, ankle-brachial index (ABI), requires specialized equipment, staff, and clinic visits.

“There exists a glaring unmet clinical need to develop technology to meet the demands of modern practice,” the researchers wrote. Further, ABI testing, introduced about 60 years ago, has has remained largely unchanged and has long-standing barriers to widespread use in primary care and under-resourced settings.

The current study originated from discussions between co-first author Mattheus Ramsis, MD, and assistant professor of medicine and medical director of cardiology informatics, and co-author Elsie G. Ross, MD, an associate professor of surgery in vascular and endovascular surgery, who noted that vascular labs conducting ABI testing often also collected toe PPG waveforms.

“The light-bulb went off for me at that moment,” Ramsis said.

PPG works by shining light into tissue and measuring backscattered light associated with blood volume changes. PPG has previously been used to identify cardiovascular and metabolic conditions including diabetes and atrial fibrillation. Earlier research efforts to use PPG for PAD detection had relied on small datasets, long recordings and less interpretable deep-learning approaches.

For their approach, the UCSD team assembled a dataset containing more than 10,000 toe PPG recordings from more than 3,500 patients who underwent ABI testing at UC San Diego Health between 2020 and 2025. Using these data, the researchers extracted 78 waveform features from the PPG signals that correlated significantly with ABI measurements. Those features were then used to train an explainable support vector machine model designed to identify PAD from PPG data alone.

Ramsis said the model correctly distinguished PAD cases approximately 83% of the time using only PPG data, compared with roughly 60% to 65% performance typically achieved using clinical risk-factor assessments alone. Incorporating smoking status of the patients further improved the performance of the new method.

Importantly, the model performed consistently across Black, Hispanic, and White patient populations, and among patients with diabetes, coronary artery disease, and end-stage renal disease. The researchers also reported similar performance across two UC San Diego Health campuses that used different equipment and staff.

The investigators noted that the physiologic basis for their findings align with established vascular biology. In PAD, reduced blood flow and arterial stiffness alter the morphology of PPG waveforms. Healthier patients demonstrated steeper systolic upstrokes and narrower waveform widths, while patients with PAD showed more dampened signals.

“Our findings support the existence of a reproducible PPG-derived digital biomarker that captures peripheral vascular pathophysiology relevant to ABI-defined PAD,” the researchers wrote.

The researchers said they don’t think their new model should replace ABI testing. Instead, they envision PPG screening as a complementary tool that could serve to identify patients earlier that might need further vascular evaluation.

The team said prospective deployment studies are already underway to evaluate performance in clinical settings and across additional reference standards, including toe pressure measurements, ultrasound imaging, and angiography. Additional research will also gauge performance in consumer-grade environments, including smartphones and wearable devices, and assess how the screening tool functions in broader patient populations outside specialized vascular clinics.

“If we can catch PAD early enough to prevent a limb amputation, that would be the ultimate impact: preserving limb function, reducing mortality, and addressing barriers in underserved populations,” Ramsis said.

The post PPG-Derived Digital Biomarker Developed for Peripheral Artery Disease Detection appeared first on Inside Precision Medicine.

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Antibody-Drug Conjugate Shows Activity in Hard-to-Treat Uterine Cancer

A Phase II clinical trial led by researchers at Yale School of Medicine and Yale Cancer Center has found that the antibody-drug conjugate sacituzumab govitecan, also known as Trodelvy, demonstrated encouraging clinical activity in patients with recurrent uterine cancer who had already exhausted several standard treatment options.

The findings, published in Clinical Cancer Research, suggest the therapy could become an important new option for patients with advanced endometrial cancer after chemotherapy and immunotherapy stop working.

A difficult disease to treat after relapse

Endometrial cancer is the most common gynecologic cancer in the United States, and rates continue to rise worldwide. While recent advances in immunotherapy have improved treatment for some patients, options remain limited once the disease returns after platinum-based chemotherapy or checkpoint inhibitor therapy.

Patients with recurrent disease often face poor outcomes, particularly those with aggressive tumor types such as uterine serous carcinoma and carcinosarcoma. Standard second-line chemotherapies typically produce modest response rates and short-lived disease control.

Researchers therefore wanted to investigate whether sacituzumab govitecan could improve outcomes in this challenging setting.

How the drug works

Sacituzumab govitecan belongs to a newer class of targeted therapies known as antibody-drug conjugates, or ADCs. These drugs combine an antibody that recognizes cancer cells with a chemotherapy payload designed to destroy them.

In this case, the therapy targets Trop-2, a protein commonly overexpressed in several aggressive cancers, including many uterine tumors. Attached to the antibody is SN-38, the active metabolite of irinotecan, a well-known chemotherapy drug.

By delivering chemotherapy directly to Trop-2–expressing cancer cells, researchers hope to increase anti-tumor activity while limiting damage to healthy tissue.

The drug is already approved for metastatic breast cancer and urothelial cancer, but remains investigational in uterine cancer.

Trial included heavily pretreated patients

The study enrolled 50 patients with recurrent or persistent endometrial cancer between 2020 and 2024. All participants had previously received platinum-based chemotherapy, and many had also undergone treatment with immune checkpoint inhibitors such as pembrolizumab or dostarlimab.

The study population represented a particularly difficult-to-treat group. Most patients had aggressive tumor histologies, including serous carcinoma, carcinosarcoma, or grade 3 endometrioid disease. Patients had received a median of two prior treatment regimens, with some undergoing as many as four lines of therapy before entering the study.

Encouraging responses and survival data

The trial met its primary endpoint, achieving an objective response rate of 28%. Two patients experienced complete responses with no detectable cancer remaining, while another 12 patients achieved partial responses with substantial tumor shrinkage.

In total, more than 70% of evaluable patients experienced some degree of tumor reduction during treatment. The study also reported durable responses, with a median response duration of 9.3 months. Several patients were still responding at the time of analysis.

Median progression-free survival reached 5.5 months, while median overall survival was 17.5 months in this heavily pretreated population.

Researchers also noted that responses were observed across multiple tumor subtypes rather than being limited to one specific histology.

“The results of our Investigator Initiated Trial complement and extend the TROPiCS-03 Trial results by demonstrating significant clinical activity of SG not only against the most common histological types of uterine cancer (endometrioid tumors) but also in patients harboring biologically aggressive endometrial tumors such as uterine serous carcinoma and carcinosarcoma,” said Alessandro Santin, MD, the study’s lead author.

Side effects remained manageable

As expected with potent cancer therapies, treatment-related side effects were common. The most frequent severe toxicities included neutropenia, anemia, fatigue, diarrhea, and febrile neutropenia.

However, investigators reported that most adverse events were manageable using supportive care measures such as growth factor support, anti-diarrheal medications, hydration, and dose adjustments. No treatment-related deaths were reported during the trial.

Looking ahead

The researchers cautioned that the study was relatively small and lacked a randomized comparison arm. Nevertheless, the results add to growing evidence supporting sacituzumab govitecan in advanced endometrial cancer, particularly for patients who have limited options remaining after standard therapies fail.

A larger international Phase III study is already underway to compare the drug directly against standard chemotherapy in patients with recurrent endometrial cancer following platinum chemotherapy and immunotherapy.

The team also highlighted future possibilities for combining the therapy with immunotherapy approaches, particularly because the drug’s chemotherapy payload may help stimulate anti-tumor immune responses.

“This is a major bench-to-bedside accomplishment for patients with uterine cancer,” Santin said.

 

The post Antibody-Drug Conjugate Shows Activity in Hard-to-Treat Uterine Cancer appeared first on Inside Precision Medicine.

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<![CDATA[Psychiatry faces surging stress-driven illness as psychedelics, lifestyle medicine, and biomarkers reshape precision diagnosis and care.]]>
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Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Background: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines. Objective: The purpose of this study is to develop a clinically interpretable model that classifies the risk levels of T1D complications—acute, chronic, and low—using real-world data and expert clinical rules derived from the Saudi Diabetes Clinical Practice Guidelines. Methods: A pediatric T1D dataset comprising of 306 patients was preprocessed through structured cleaning and feature engineering. Risk labels were constructed using Saudi Diabetes Clinical Practice Guidelines–derived rules. Feature selection was performed using a hybrid approach that combined the SHAP (Shapley Additive Explanations) analysis with exhaustive feature selection. A decision tree model was trained and optimized via cross-validation, using the -score as the primary performance metric. Results: The final model achieved a high mean -score of 0.9876 with a low variance of 0.0189, using only 5 clinical features: BMI, hypoglycemia, disease duration, hemoglobin A, and impaired glucose metabolism. These features were consistently ranked as the most influential. The resulting decision tree offered a transparent logic path, enhancing its clinical interpretability and usability. Conclusions: This study demonstrates that a simple and interpretable model, guided by national clinical guidelines, can effectively predict the risk levels of T1D complications in children and adolescents. Its strong performance, clarity, and reliance on a small number of clinically meaningful features make it a promising candidate for integration into clinical decision support systems. This supports a shift toward predictive and personalized diabetes care.
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Single Psilocybin Dose Relieves Depression for Over Three Months

Researchers in Sweden report that a single dose of psilocybin, a psychedelic compound found in mushrooms, can provide rapid relief from depressive symptoms. Results from a small-scale Phase II trial, published today in JAMA Network Open, show that patients experienced an improvement as soon as two days after treatment, with effects persisting for longer than three months. 

”Our results suggest that psilocybin can provide rapid, clinically meaningful improvement in depression and may serve as an alternative to standard treatment when fast symptom reduction is important,” says Hampus Yngwe, MD, consultant psychiatrist and PhD student at the department of clinical neuroscience of the Karolinska Institutet in Stockholm. 

Major depressive disorder is commonly treated with selective serotonin reuptake inhibitors (SSRIs), but most patients do not respond to this treatment or become resistant. In addition, their effects can typically take several weeks to be noticeable, and side effects are common. 

Previous research had shown that a single dose of psilocybin can have antidepressant effects in people with treatment-resistant depression or anxiety disorders in patients with advanced cancer. The current study looked instead at the effects of this compound on “common” forms of major depressive disorder. 

The study recruited a total of 35 people with moderate to severe recurrent depression, between 20 and 65 years of age. They were randomly assigned to receive either a single 25mg dose of psilocybin or niacin, an active placebo that causes a noticeable physical reaction. All patients received psychotherapeutic support before, during, and after treatment.  

Participants were evaluated using the Montgomery–Åsberg depression rating scale (MADRS) to assess treatment effects at multiple time points after dosing. After a week, the group who received psilocybin saw an average MADRS score reduction of 9.7 points, compared to 2.4 points in the placebo group, and these effects persisted after two weeks and six weeks. At this point, 53% of participants who received psilocybin were in remission, compared to 6% in the placebo group. 

A self-reported version of the MADRS revealed that patients saw antidepressant effects as early as day two after dosing, and continued to experience these positive effects for over three months. 

A year after treatment, all patients who received psilocybin treatment remained in remission. However, many of the patients who received the placebo had also recovered at that point, showing no major statistical difference between both groups. 

“The long-term effects are uncertain,” says Yngwe. “Repeated treatments may be needed to prevent relapse. This needs to be investigated in larger studies.”

Because the effects of psilocybin are strong and easily recognizable, many participants were able to tell whether they had received the treatment or the placebo. This is a common challenge scientists face when studying psychedelic treatments that can make it difficult for patients and researchers alike to separate the effects of the treatment from their expectations. “We want to understand how factors such as treatment expectations and lack of blinding affect the results, as previous studies may have exaggerated the treatment effects,” notes Yngwe.

Next, the researchers will analyze data from PET scans, blood, and cerebrospinal fluid samples collected from all patients before and after dosing. This will help them understand the physiological changes induced by psilocybin, and how these influence its observed antidepressant effects. 

”Research suggests that the interaction between parts of the brain is impaired in depression and that this may be linked to changes in the connections between nerve cells, known as synapses,” says Yngwe. “In preclinical studies, psychedelics have been shown to stimulate synaptic growth. We therefore want to investigate whether psilocybin alters synaptic density in the brain.”

The post Single Psilocybin Dose Relieves Depression for Over Three Months appeared first on Inside Precision Medicine.

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Large Language Models and Their Applications in Mental Health: Scoping Review

Background: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. Objective: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. Methods: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. Results: In total, 41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
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STAT+: Takeda will pay $13.6 million to settle allegations it paid kickbacks to doctors

Takeda Pharmaceuticals agreed to pay $13.6 million to settle allegations that it paid kickbacks to doctors to prescribe an antidepressant and, consequently, violated federal law by causing Medicaid to pay false claims, the U.S. Department of Justice said in announcing the settlement.

From January 2014 to October 2020, the company allegedly offered speaking fees and paid for meals at “high-end” restaurants to persuade physicians to prescribe Trintellix. Moreover, certain doctors who attended multiple programs on the same topic and received meals and drinks did not gain any educational benefit from attending the programs.

“This settlement demonstrates the continued commitment of my office to ensure that patients’ best interests remain paramount,” Eric Grant, the U.S. attorney for the Eastern District of California, said in a statement. “Prescribing decisions should not be influenced by drug companies’ payments or side perks made available to physicians.”

Continue to STAT+ to read the full story…

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A Novel Haptic Cardiac Simulator: Mixed Methods Pilot Evaluation in Medical Students and Educators

Background: Cardiac auscultation is an essential component of clinical examination but is often challenging to achieve proficiency in. Self-contained, multisensory learning resources that incorporate simultaneous visual and haptic stimuli offer a unique approach to supporting learners in acquiring this core skill. Objective: This pilot study of both medical students and clinical educators evaluated the utility of a novel iPhone app, Haptic Heart, which generates haptic vibrations to simulate heart sounds and murmurs. We aimed to explore the perceptions of students and educators when using haptics as a learning resource and the underlying reasons behind these perceptions and to gather lessons that would inform future development of the resource. Methods: Clinical-year medical students from the Lincoln Medical School with access to an iPhone were invited to trial Haptic Heart between October 2023 and December 2024. Cardiology specialists involved in clinical education were also invited to take part. After using the app, participants were asked to complete a modified version of the 12-item Evaluation of Technology-Enhanced Learning Materials: Learner Perceptions questionnaire that included additional free-text items. Educators were also asked to comment on the resource’s authenticity and perceived usefulness. Quantitative responses were analyzed using descriptive statistics; free-text responses were analyzed for common themes. Results: A total of 21 students and 18 educators completed the evaluation. Both cohorts returned positive responses across nearly all questionnaire items, with students showing near universal agreement that the app was of excellent quality (21/21, 100%), supported their learning needs (21/21, 100%), and would change their clinical practice (20/21, 95.2%). Educators similarly rated the resource highly for learning utility (16/18, 88.9%) and authenticity (13/18, 72.2%). Reported technical difficulties were minimal for students (1/21, 4.8%) and educators (2/18, 11.1%). Analysis of free-text responses suggested that learners valued the ability to “feel” murmurs and to vary heart rate. Educators highlighted the resource’s novelty and innovation, although some noted concerns about audio quality when using a stethoscope to auscultate haptic vibrations directly. Conclusions: This pilot evaluation demonstrates the potential of smartphone-based haptic technology as a tool for medical education. Haptic Heart was perceived by both students and educators as an innovative educational tool for cardiac auscultation. Further work should focus on expanding the range of haptic patterns provided and exploring the effectiveness of these resources on learning.
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