Category: 8. Health

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A new study, published recently in Nature Communications, offers the first-ever map of which parts of Chikungunya virus trigger the strongest response from the body’s T cells. 

With this map in hand, researchers are closer to developing Chikungunya vaccines or therapies that harness T cells to strike specific targets, or “epitopes,” to halt infection. The new study also offers important clues for understanding why many people experience chronic, severe joint pain for years after clearing the virus.

“Now we can see what T cells are seeing patients with chronic disease,” says LJI Assistant Professor Daniela Weiskopf, PhD, senior author of the new study.

This research comes as many mosquito-borne viruses, including Chikungunya, are moving into new areas of the globe.

“Historically, Chikungunya was considered an emerging virus. Now all of Latin America has been exposed,” says Weiskopf. “These mosquitoes are traveling further north, and we need to know what’s going on with this virus before it arrives in the United States.”

T cells jump into action

Chronic Chikungunya virus disease strikes between 30 to 60 percent of those infected—usually women—and causes severe joint pain. This debilitating joint pain can last for years following the initial viral infection. 

In a study out earlier this year, Weiskopf and her colleagues showed that these patients have a population of inflammatory CD4+ T cells that closely resembles the T cell signature of rheumatoid arthritis, an autoimmune disease.

“So many people, mostly women, have chronic disease following Chikungunya virus infection,” says Weiskopf. “This has an impact on the workforce and impacts the economy. And there’s no treatment.”

Weiskopf and her colleagues are working to understand why these CD4+ T cells linger and cause problems long after a person clears the virus. For this study, they investigated whether people who develop chronic disease produce T cells that naturally target a different set of epitopes on Chikungunya virus.

Would a different “flavor” of T cells be more likely to stay in the body after infection?

Weiskopf and her team used a “peptide pool” approach to assemble a map of key T cell epitopes on Chikungunya virus. The researchers broke up the virus into very small amino acid sequences, called peptides. Then they took T cells from people with chronic Chikungunya virus disease and exposed these cells to the pool of peptides.

By stimulating the T cells, the researchers discovered exactly which parts of the virus are most likely to be recognized by T cells. These “immunodominant” regions may prove to be good targets for future Chikungunya treatments.

Rimjhim Agarwal, a UC San Diego graduate student and member of the Weiskopf Lab, spearheaded experiments to learn more about these T cells. Agarwal received funding from The Tullie and Rickey Families SPARK Awards for Innovations in Immunology to take a closer look. 

For her project, funded through the generosity of the Rosemary Kraemer Raitt Foundation Trust, Agarwal compared CD4+ T cells from people with chronic Chikungunya virus disease to people who cleared the virus quickly with no lasting symptoms.

Agarwal found that both patient groups had T cells that targeted the same viral epitopes. People who developed chronic disease did not recognize different proteins of the virus.

Now the question is—why do these T cells stick around to cause inflammation in some but not all people? Weiskopf and Agarwal are now looking at where Chikungunya virus might hide in the body to stimulate a long-term T cell response.

The LJI team also hopes to help other laboratories shed light on how to fight the virus. “Identifying the immunodominant T cell epitopes could seed new research into Chikungunya-specific T cell responses,” says Agarwal.

Reference: Agarwal R, Ha C, Côrtes FH, et al. Identification of immunogenic and cross-reactive chikungunya virus epitopes for CD4+ T cells in chronic chikungunya disease. Nat Commun. 2025;16(1):5756. doi: 10.1038/s41467-025-60862-7

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The study, published in The International Journal of Cancer, covers nearly 2.5 million children born in Sweden during two periods, 1982 to 1989 and 1999 to 2015. Of these, 15.5 percent were born by C-section, i.e., nearly 376,000 children. In the entire group, 1,495 children later developed leukaemia. 

Using the Medical Birth Register, the researchers were able to divide the C-sections into planned and emergency cesarean sections. The children who later developed leukaemia were overrepresented in the group born by planned C-section. In particular, the risk of the most common form of childhood leukemia, acute lymphoblastic leukemia (ALL), increased. 

The risk of ALL was 21 percent higher in children born by planned C-section compared with children born vaginally.

Risk is higher in boys than girls

The risk of developing the most common form of ALL, B-cell acute lymphoblastic leukemia (B-ALL), was 29 percent higher in those born by planned C-section. 

The increased risk remained even when the researchers adjusted for other relevant factors in mothers and children. The increased risk was more pronounced in boys than in girls and among younger children.

The researchers emphasise that the risk remains low, regardless of the mode of delivery. Between 50 and 70 Swedish children per year are diagnosed with B-ALL. The excess risk associated with planned C-sections corresponds to approximately one case of B-ALL per year, according to the researchers behind the study.

“C-sections are an important and often life-saving part of obstetric care. We don’t want mothers to feel anxious about medically indicated C-sections. But when this result is combined with other study results showing that the risk of later asthma, allergies or type 1 diabetes increases in children born by planned C-section, there is reason to discuss C-sections that are not medically indicated,” says Christina-Evmorfia Kampitsi, researcher at the Institute of Environmental Medicine, Karolinska Institutet, and lead author of the study.

The researchers discuss possible mechanisms that could explain why it is planned and not emergency C-sections that carry an increased risk of certain diseases, all of which are related to immunological factors. The reasoning is that emergency cesarean sections usually begin as a vaginal delivery. This causes stress for the baby and exposure to vaginal bacteria if the amniotic sac has ruptured.

Stress and vaginal bacteria may explain difference

However, in planned C-sections, which are usually performed before labour has started naturally, the baby does not experience this stress and is not exposed to vaginal bacteria. The researchers suggest that this difference may help explain the increased risk of ALL, and believe that the study may contribute to a better understanding of what causes ALL in children.

Some of the results did not reach the threshold for statistical significance, meaning that chance cannot be entirely ruled out.

“Fortunately, ALL is rare. This means that many C-deliveries are needed to obtain a statistically significant result, and it is difficult to obtain such a large sample in a Swedish registry study. However, the results are close to significant, are in line with what previous studies have shown, and remain when we adjust for other relevant factors, which still makes them relevant,” says Christina-Evmorfia Kampitsi. 

The research is funded by the Swedish Research Council and the Swedish Cancer Society. The researchers state that there are no conflicts of interest.

Reference: Kampitsi CE, Mogensen H, Heyman M, Feychting M, Tettamanti G. Mode of delivery and the risk of lymphoblastic leukemia during childhood—A Swedish population-based cohort study. Int J Cancer. 2025. doi: 10.1002/ijc.70027

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The worldwide increase in Type 2 diabetes cases is a major public health concern, currently impacting more than 6% of the adult population—a number projected to reach approximately 7% by 2030.

According to the World Health Organisation (WHO), Type 2 diabetes is mostly preventable. Important preventive measures include staying at a healthy weight and participating in consistent physical activity, both of which can help prevent prediabetes—characterised by high blood sugar and insulin resistance—from developing into full-blown diabetes.

A recent study published in Cardiovascular Diabetology – Endocrinology Reports adds to the growing evidence that exercise plays a crucial role in reducing the risk of developing type 2 diabetes.

The findings suggest that engaging in just over two hours of exercise per week may help individuals with prediabetes reverse the condition and prevent it from advancing.

Dr David Cutler, a board-certified family medicine physician at Providence Saint John’s Health Centre in Santa Monica, CA, who was not involved in the study, explained this in an interview with Medical News Today:

“The health impact of having diabetes is profound. There is increased risk of almost every category of disease: heart attack, stroke, kidney failure, vascular disease, blindness, and infection. These ailments lead to earlier death and markedly impaired quality of life prior to death for diabetics. While prediabetes carries little of these increased risks, it is a warning because we know that 25–50% of prediabetics will develop diabetes. So, the smart thing to do is prevent prediabetes or reverse it if it is already present.”

Engaging in over 150 minutes of physical activity per week significantly increased the chances of reversing prediabetes, making individuals four times more likely to return to normal blood sugar levels.

“The study clearly reinforces what physicians have been telling patients for a long time: Diet, weight control and exercise are the key ingredients to a long and healthy life. And now you can have a concrete target to shoot for, 150 minutes of exercise per week,” said Cutler.

In addition to regular exercise, maintaining HbA1c levels below 6.0% was identified by researchers as a key factor in boosting the likelihood of reversing prediabetes.

Data source and study population

The UK Biobank is a large-scale, population-based prospective cohort study that included over half a million individuals between 2006 and 2010 at 22 assessment centers across the UK. Study participants were enrolled from diverse regions across England, Scotland, and Wales [18, 19]. During enrolment, participants completed a comprehensive touchscreen survey, underwent physical assessments, and submitted biological samples. Additionally, the UK Biobank includes data from the UK’s electronic health records; further details regarding the UK Biobank can be found online (http://www.ukbiobank.ac.uk). Ethical approval for research activities conducted within the UK Biobank was granted by the Research Ethics Committee (reference 11/NW/0382), with participants providing duly informed written consent. We applied for and were granted permission to use data from the UK Biobank (Application ID: 104830).

During the baseline period from 2006 to 2010, detailed data were collected on demographic covariates, lifestyle, disease diagnosis information, and blood test indicators (e.g., leukocyte count, lipid profile, fasting glucose). From the initial cohort of 502,366 individuals, we excluded individuals with: (1) preexisting diagnosis of ACD, AD or VD at baseline (n = 234), (2) missing data on baseline CMDs (HD, stroke, or T2D) (n = 1,852), and (3) other incomplete covariates information (e.g., demographic characteristics, laboratory test result, and clinical covariates) (n = 212,532). After these rigorous exclusion criteria were applied, a cohort of 287,748 individuals was retained for primary analyses. Sensitivity analyses were conducted in five subsets: (1) 287,682 participants after excluding dementia cases within 2 years of baseline; (2) 287,384 participants after excluding dementia cases within 5 years of baseline; (3) 284,118 participants after excluding those with encephalitis, meningitis, multiple sclerosis, previous subarachnoid/intracerebral hemorrhage, or chronic central nervous system infections; (4) 284,985 participants after excluding participants without available polygenic risk scores (PRS) for dementia; (5) competing risk model treating all-cause mortality as a competing event (Fig. 1).

Assessment of cardiometabolic diseases

CMDs that included HD (including acute ischemic heart disease, chronic ischemic heart disease, atrial fibrillation, and heart failure), stroke, and type 2 diabetes [14] were ascertained via self-reported medical history, primary care records, and hospital admission records with International Classification of Diseases (ICD)-10 coded diagnoses (the field codes were shown in Table S1). A CMD diagnosis required confirmation by at least one objective source (primary care records or ICD codes). CMD status was determined based on the total number of CMDs at baseline, and categorized as CMD-free, single CMD (such as HD alone, stroke alone, or T2D alone), or CMD multimorbidity (i.e. two or more comorbid CMDs). Furthermore, CMD multimorbidity was further stratified into the following subtypes based on distinct combinations: HD + stroke, HD + T2D, stroke + T2D, and HD + stroke + T2D [9].

Outcomes

The primary outcome of this study was ACD; the secondary outcomes were AD and VD. Dementia outcomes were defined according to algorithms developed and validated by the UK Biobank as ACD (Field 42018), AD (Field 42020) and VD (Field 42022). The follow-up time was determined as the earliest of the following: the moment of the registered death, the occurrence of the outcome event, or the point at which the ultimate outcome occurred (on or before April 30, 2024).

Covariates

The following covariables were included as covariables in the analysis: age at baseline (years), sex (male/female), ethnicity (white/not white), education level (high [college or university degree]/intermediate [A levels/AS levels or equivalent, or O levels/GCSEs or equivalent]/low [none of the aforementioned]) [20], duration of moderate activity (low/intermediate/high) [21], body mass index (BMI), hypertension (yes/no), depression (yes/no), smoking (never/previous/current), and drinking status (never/previous/current). Otherwise, we collected the following data including Townsend deprivation index (TDI), sleep duration (< 6 h/night, 6–9 h/night, and > 9 h/night), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), hemoglobin A1c (HbA1c), and fasting glucose. The calculation of BMI involves the division of an individual’s weight in kilograms by the square value of the height measured in centimeters. These baseline data were recorded upon enrolment. The standardized PRS for AD, constructed using UK Biobank genotype data (post-imputation and quality control), served as a quantitative measure of genetic susceptibility to dementia. This PRS, previously validated in studies of AD heritability and risk stratification [22, 23], directly correlates with increasing genetic predisposition (higher scores indicating elevated risk). The PRS was categorized into quintiles: low genetic risk (quintile 1), moderate genetic risk (quintiles 2–4), and high genetic risk (quintile 5).

Mediators

Inflammatory and metabolic biomarkers were obtained from baseline blood samples collected at the time of enrollment in the UK Biobank. Inflammatory markers included: leukocyte, lymphocyte, monocyte, neutrophil, platelet, C-reactive protein (CRP), systemic inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Metabolic markers included: glucose, hemoglobin A1c, insulin-like growth factor 1, LDL-C, HDL-C, triglycerides, total cholesterol, albumin, uric acid, triglyceride glucose index (TyG), TyG-body mass index, TyG-waist circumference, waist circumference, and BMI. We collected the above indicators based on their associations with dementia reported in previous studies [24,25,26,27,28,29,30,31,32,33,34,35,36].

Statistical analysis

Baseline characteristics are reported as percentages, mean ± standard deviations, or medians with interquartile ranges on the basis of the individual variable characteristics. The study population was stratified into three groups based on CMD status for baseline characteristic description: CMD-free, single CMD, and CMD multimorbidity. Additionally, we further categorized the CMD multimorbidity group into four subgroups based on different combinations of conditions: HD + stroke, HD + T2D, stroke + T2D, and T2D + HD + stroke, and described their baseline characteristics. Categorical data were analyzed via either the chi-square test or Fisher’s exact test, whereas continuous data were evaluated using one-way ANOVA or the Kruskal‒Wallis test, depending on each method’s suitability. Participants with missing data on cardiometabolic diseases (HD, stroke, T2D) at baseline or covariates (e.g., demographic characteristics, laboratory results and clinical covariates) were excluded. The missing PRS values (0.96%) were imputed using multiple imputation by chained equations to minimize information loss.

CMD-free status was used as a reference. Kaplan–Meier (KM) survival analysis and the Cox proportional hazards model were utilized to estimate the hazard ratios (HRs) of CMD status in relation to different outcomes (ACD, AD, and VD). The model was adjusted for age (continuous, in years), sex (male/female), ethnicity (white/not white), education level (high/intermediate/low), duration of moderate activity (low/intermediate/high), BMI (continuous, kg/m²), hypertension (yes/no), smoking status (never/previous/current), and drinking status (never/previous/current). Given the established impact of depression on cognitive function [37, 38], we further adjusted for depression status in our multivariable models. The proportional hazards assumption was examined via Schoenfeld residuals. The violation of proportionality was met for the sex variable in the model for ACD and AD, therefore, the Cox model was employed with stratification by sex. P-values for trends were determined through the application of the Cochran‒Armitage trend test. The outcomes are presented as hazard ratios (HRs) with their corresponding 95% confidence intervals (95% CIs).

Furthermore, we investigated whether the genetic predisposition to AD modifies the association between CMD multimorbidity and the risk of AD. We employed a multivariate Cox proportional hazards model to evaluate the correlation between CMD multimorbidity and the PRS for AD.

We performed subgroup analyses with survival analysis model to assess heterogeneity across different categories according to age (≥ 60 or < 60 years), sex (male or female), smoking status (never, previous or current), drinking status (never, previous, current), ethnicity (white or not white), education (high, intermediate, or low), duration of moderate activity (low, moderate, or high), and sleep duration (< 6 h/night, 6–9 h/night, or > 9 h/night).

A mediation analysis was applied to explore whether inflammation and metabolism-related indicators could mediate the associations between CMD multimorbidity and dementia. First, a generalized linear model was employed to explore the potential correlations between CMD status and inflammation and metabolism-related markers. Next, Cox proportional hazard regression analyses were conducted to evaluate the relationships between these biomarkers and dementia outcomes. Finally, a mediation analysis was performed for the variables that showed a significant association in the aforementioned analyses. In this study, the average direct effect refers to the direct impact of CMDs on dementia. The average causal mediation effect refers to the partial effect mediated by inflammatory or metabolic factors. The proportion of the mediation effect represents to the percentage of indirect effect to the total effect. The covariates of the intermediary model were adjusted according to the covariates of the survival analysis model.

Additionally, we conducted sensitivity analyses to evaluate the stability of the link between CMD status and dementia. In order to minimize the risk of reverse causation, we initially omitted individuals who received a dementia diagnosis within the first 2 years or 5 years after their enrolment. Additionally, sensitivity analysis was conducted after excluding patients with a history of encephalitis, meningitis, multiple sclerosis, motor neuron disease, subdural/subarachnoid hemorrhage, and chronic central nervous system infection. To validate robustness, we excluded participants with missing PRS data and repeated the sensitivity analysis. Furthermore, we employed a competing risk model to address potential mortality bias by treating all-cause mortality as a competing event.

Statistical analyses were conducted in (R version 4.4.1) and SPSS (version 27.0). In all cases, the criterion for statistical significance was set at a two-tailed P-value < 0.05.

Convenience and accessibility are two essential ingredients in any effective workout regimen, ensuring you’re able to stay the course and see long-term benefits.

And that’s exactly what online trainer Rachael Sacerdoti is serving up with her new 14-Day Mini Movement Challenge launching on Monday, July 7 on her Instagram channel.

Despite concerns about cognitive decline after cancer treatment, most breast cancer survivors show no increased risk of developing Alzheimer’s disease, and some may have a slightly lower risk than their cancer-free peers, according to a large retrospective study from Korea.

However, any apparent protective effect faded with time, the investigators reported online in JAMA Network Open.

Overall, this is “reassuring news for cancer survivors,” Tim Ahles, PhD, a psychologist with Memorial Sloan Kettering Cancer Center, New York City, who wasn’t involved in the study, told Medscape Medical News.

“I get this question from patients a lot,” Ahles said. And based on these findings, “it doesn’t look like a history of breast cancer and breast cancer treatment increases your risk for Alzheimer’s disease.”

Breast cancer survivors often report cancer-related cognitive impairment, such as difficulties with concentration and memory, both during and after cancer treatment. But evidence surrounding patients’ risk for Alzheimer’s disease is mixed. One large study based in Sweden, for instance, reported a 35% increased risk for Alzheimer’s disease among patients diagnosed with breast cancer after the age of 65 years, but not among younger patients. A population-based study from Taiwan, however, found no increase in the risk for dementia overall compared with cancer-free individuals but did note a lower dementia risk in patients who had received tamoxifen.

To help clarify the evidence, investigators assessed Alzheimer’s disease risk in a large cohort of patients and explored the association by treatment type, age, and important risk factors.

Using the Korean National Health Insurance Service database, the researchers matched 70,701 patients who underwent breast cancer surgery between 2010 and 2016 with 180,360 cancer-free control individuals.

The mean age of breast cancer survivors was 53.1 years. Overall, 72% received radiotherapy. Cyclophosphamide (57%) and anthracycline (50%) were the most commonly used chemotherapies, and tamoxifen (47%) and aromatase inhibitors (30%) were the most commonly used endocrine therapies.

The primary outcome of this study was the incidence of newly diagnosed Alzheimer’s disease, which was defined on the basis of at least one prescription for medications to manage dementia associated with Alzheimer’s disease (donepezil, rivastigmine, galantamine, or memantine).

During a median follow-up of about 7 years, 1229 newly diagnosed Alzheimer’s disease cases were detected in breast cancer survivors and 3430 cases in control individuals — incidence rates of 2.45 and 2.63 per 1000 person-years, respectively.

This corresponded to an 8% lower risk for Alzheimer’s disease in breast cancer survivors compared with cancer-free control individuals at 6 months (subdistribution hazard ratio [SHR], 0.92; 95% CI, 0.86-0.98). The association was especially notable in survivors older than 65 years (SHR, 0.92; 95% CI, 0.85-0.99).

Looking at individual treatment modalities, only radiation therapy was associated with significantly lower risk for Alzheimer’s disease among breast cancer survivors (adjusted HR [aHR], 0.77).

Several risk factors were associated with a significantly higher risk for Alzheimer’s disease: current smoker vs never or ex-smokers (aHR, 2.04), diabetes (aHR, 1.58), and chronic kidney disease (aHR, 3.11). Notably, alcohol use, physical activity level, and hypertension were not associated with Alzheimer’s disease risk.

However, any potential protective effect may be short-lived. The reduced risk for Alzheimer’s disease was no longer significant at 1 year (SHR, 0.94; 95% CI, 0.87-1.01), 3 years (SHR, 0.97; 95% CI, 0.90-1.05), or 5 years (SHR, 0.98; 95% CI, 0.89-1.08).

Even so, breast cancer survivors can still feel reassured by the findings.

“Concerns about chemobrain and the long-term adverse effects of breast cancer treatment on cognition are common, but our findings suggest that this treatment does not directly lead to Alzheimer’s disease,” wrote the authors, led by Su-Min Jeong, MD, with Seoul National University College of Medicine, Seoul, South Korea.

Ahles agreed. The general takeaway from this study is that there is “no strong evidence that the cancer treatment is going to increase your risk for developing Alzheimer’s,” Ahles said. When patients ask about the risk for Alzheimer’s disease, “I can say, ‘Here’s yet another new study that supports the idea that there’s no increased risk.’”

He cautioned, however, that the study doesn’t address whether people with a genetic predisposition to Alzheimer’s might develop it sooner due to cancer treatment.

“Does the cancer treatment increase your probability or nudge you along? The study doesn’t answer that question,” Ahles said.

The study reported having no commercial funding. Jeong and Ahles reported having no relevant disclosures.

This cross-sectional study reports the prevalence and genotype distribution of human papillomavirus infection among women in Yangpu District, Shanghai, China from 2020 to 2024. The overall HPV infection rate detected in this study was 23.10%. Compared with similar studies in China, this infection rate is higher than the 21.0% reported in Beijing [11], 21.97% in Jinshan District Shanghai [12], 22.82% in Luoyang Henan Province [13], and 17.92% in Zhoupu District Shanghai [14], but lower than the 41.04% in Hangzhou Zhejiang Province and 50.64% in Tianjin [15, 16]. This discrepancy may be associated with variations in regional epidemiological characteristics or demographic composition of study populations [17]. Previous studies have reported substantial heterogeneity in HPV prevalence across China, ranging from 6.2–50.64% [16, 18]. Prevalence rates in clinical populations significantly exceed those in community-based screening cohorts, likely reflecting health-seeking behaviors related to symptomatic presentation that differ fundamentally from asymptomatic screening populations [19]. In the present study, an HPV prevalence of 10.5% was observed among general screening populations, lower than the 13.6% rate reported in routine screening cohorts from Zhejiang Province [20].

This regional analysis identified HPV-52 as the predominant high-risk genotype, followed by HPV-53, -58, -51, -39, and − 68, aligning with patterns reported in other Chinese studies. HPV-52, -16, -58, -51, and − 66 represent predominant subtypes in Beijing [11], while HPV-52, -16, -58, -51, and − 53 dominate in Jinshan District, Shanghai [12]. Similarly, HPV-52, -58, -16, -53, and − 51 constitute major subtypes in the Golden Triangle region of Fujian, China [3]. Substantial evidence confirms that beyond HPV-16 and − 18, genotypes including HPV-52, -58, -51, and − 53 significantly contribute to cervical carcinogenesis [21]. This study’s findings exhibit discrepancies in subtype prevalence rankings compared with certain Chinese regions [22], indicating significant geographical heterogeneity in high-risk HPV genotype distributions. This epidemiological variation likely originates from multifactorial mechanisms: Firstly, socio-behavioral patterns—including number of sexual partners, marital/reproductive history, and sexual health literacy—fundamentally shape subtype distributions by modulating viral transmission routes and exposure frequency. Secondly, molecular interactions between HPV genotypes and region-specific host immunogenetic backgrounds may establish distinct infection profiles in defined populations [23].

From the perspective of infection patterns, HPV infections exhibit a phenomenon of multi-subtype coexistence. Beyond single-subtype infections, mixed-infection modes are particularly prominent, with dual infections being the predominant form. It remains unclear whether co-infection with multiple HPV genotypes involves competitive or synergistic relationships. However, existing studies indicate that mixed infections with multiple types increase the risk of cervical cancer more significantly than single-genotype infections [24, 25]. A study conducted in Mexico observed a correlation between multiple HPV infections and high viral load as well as infection persistence [26]. Research from South Korea demonstrated that patients with multiple HPV infections had longer viral clearance cycles compared to those with single-type infections [27]. The mechanisms and potential carcinogenic effects of multiple infecting genotypes require further investigation.​​ Fig. 2 results revealed that HPV-52, HPV-53, and HPV-58 were the most common genotypes in co-infections among women in this study, with HPV-52 + HPV-53 and HPV-52 + HPV-58 co-infections being the most frequent combinations. In Shanghai, China, HPV vaccination began in 2017, and Yangpu District started the program in 2018. Initially, the bivalent vaccine targeting HPV-16 and − 18 and the quadrivalent vaccine covering HPV − 6, -11, -16, and − 18 were mainly provided. In October of the same year, the nine-valent HPV vaccine, which covers more high-risk types including HPV − 6, -11, -16, -18, -31, -33, -45, -52, and − 58, was officially made available for vaccination. Vaccination mainly relies on the principle of self-payment and voluntary participation, and the overall vaccination rate remains relatively low. Among the HPV vaccines available on the market, only the nine-valent vaccine covers HPV-52 and HPV-58, making it more suitable for women in Yangpu District, Shanghai.

Given that the HPV-53 vaccine is still in the preclinical research stage, its high prevalence in certain regions further highlights the insufficiency of the existing vaccine’s coverage. Currently available vaccines are all based on HPV L1 self-assembled virus-like particles (VLPs), which can prevent HPV infection by inducing the production of specific neutralizing antibodies in the body [28]. Although these vaccines have shown significant efficacy, they still have several limitations, including a limited coverage of HPV types, the need to produce VLPs of different types separately before mixing them, and a high dependence on cold chain transportation and storage. The high prevalence of HPV-53 indicates the insufficiency of the current vaccine strategy in terms of protection spectrum. Therefore, in the future, it is urgent to expand the vaccine coverage through technological innovation, such as developing cross-protective vaccines based on L2 protein or constructing new multivalent vaccine systems using DNA and mRNA platforms. Although these emerging technologies face challenges in terms of research and development difficulty and cost control, they have significant potential in improving the broad-spectrum and accessibility of vaccines. The development of vaccines targeting HPV-53 is expected to provide more targeted prevention measures for regions with high prevalence.

Multiple studies have confirmed that HPV infection rates in China exhibit a U-shaped curve distribution with age, characterized by two distinct peaks [11, 24]. In this study, the first peak occurred in the under-20 age group, which may be associated with evolving sexual attitudes among youth in the current social context. A large-scale cross-sectional survey of Chinese women aged 15–24 revealed that the median age of first sexual intercourse in this cohort was 17 years [29]. Another epidemiological study demonstrated that within two to three years after initial sexual activity, HPV infection rates among adolescents can reach 50–80%, with a corresponding 2.41-fold increase in infection risk [30]. Although the sample size of the under-20 population in this study was limited, their significant disease risk profile indicates the necessity of HPV screening for this age group.​​ The second peak emerged in the 61–70 age group, likely attributed to age-related declines in immune function and hormonal changes, which increase HPV susceptibility and reduce viral clearance capacity [31, 32]. The differential infection patterns across age groups underscore age as a critical influencing factor for HPV infection, necessitating age-specific screening strategies. Particularly in the 61–70 cohort, the marked elevation in HPV infection rates suggests these individuals should be prioritized as key surveillance targets.​.

Figure 3 results show that HPV-52, HPV-53, and HPV-58 were the predominant subtypes, with their infection rates maintaining high levels across all age groups. The peak infection rates occurred in the ≤ 20 years and 61–70 years age groups, but positive infections for various HPV types were primarily concentrated in the 31–40 years, 51–60 years, and 61–70 years age groups. This indicates that the population actively undergoing gynecological examinations is mainly composed of middle-aged and elderly women, which is associated with increased awareness of HPV infection among this demographic due to the introduction and promotion of HPV vaccines in recent years [22].​​ From a prevention and control strategy perspective, HPV vaccination and screening are core measures to reduce cervical cancer risk. Data demonstrate that the earlier young women receive the HPV vaccine, the higher the antibody titer and the better the protective effect of the vaccine [33]. However, HPV vaccination coverage in China remains critically low at approximately 2.64–11.0%, significantly below immunization rates observed in most other countries [8, 34, 35]. Multiple obstacles have led to this low vaccination rate. Among female college students, insufficient awareness of the risk of HPV infection and concerns about the safety and efficacy of the vaccine are the main obstacles [36]. Concurrently, current vaccine shortages and exclusion from the national immunization program likely impede or delay individual vaccination decisions [37]. Consequently, stratified immunization and screening strategies informed by age-specific prevalence patterns should be implemented to address differential HPV exposure across age cohorts.

This study constitutes a cross-sectional analysis based on HPV testing data from 2020 to 2024 at Shanghai Yangpu District Shidong Hospital, systematically elucidating the epidemiological characteristics of HPV in Yangpu area. It supplements the multi-center epidemiological database of Shanghai and provides scientific evidence for regional prevention policies. However, the following limitations exist: Firstly, the study cohort solely comprised hospital patients within this five-year timeframe, which is not representative of the typical local population. Secondly, HPV vaccination history was not incorporated into the dataset, whereas existing evidence indicates that vaccination status significantly influences the infection spectrum distribution of high-risk HPV subtypes. Finally, the absence of synchronized collection of cervical cytology or histopathological diagnosis results restricted the analysis of associations between HPV genotype distribution patterns and cervical lesion severity. As a retrospective single-center study, our research sample included only patients seeking hospital care. This design excludes potentially infected individuals within the community who did not present for medical attention. This may limit the generalizability of our findings to the broader community population. Therefore, caution is warranted when extrapolating these conclusions to wider populations. Future investigations should expand sample size, enhance data dimensionality, and adopt multi-center collaborative models to systematically explore the association mechanisms between HPV infection and cervical lesions in Yangpu area.​.

Study design

We are planning a monocentric, double-blind, sham-controlled, randomized clinical trial to evaluate the efficacy and safety of rTMS in in children and adolescents diagnosed with ASD. The study will involve 40 patients randomized into active rTMS and sham stimulation groups. The duration of the study will be 36 months. Participants will be assessed before rTMS (T0), immediately post-treatment (T1), and at a 1-month follow-up (T2) (Fig. 1). Assessments will evaluate neuropsychological function, mainly executive functions; the severity of ASD clinical symptoms; and safety and tolerability. Additionally, biological samples, including urine and blood, will be collected at each assessment to measure biomarker changes.

Ethical issues

A participant information sheet will be provided, and informed consent will be obtained from the parents or caregivers of study participants (see Supplementary File 1). The study protocol has been reviewed and approved by the Independent Ethics Committee of Policlinico “Riuniti” of Foggia (reference number 50/CE/2023). The trial has been registered with ClinicalTrials.gov under the identifier NCT06069323. The study will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice, and applicable regulatory requirements. To ensure appropriate oversight, key study staff will meet monthly to review trial conduct, participant recruitment, protocol adherence, data quality, and any adverse events. Given the low-risk nature of the rTMS intervention, a formal independent Data Monitoring Committee was not established. Instead, trial oversight, including safety and data integrity, will be managed internally by the study team. Any serious adverse events or protocol deviations will be reported to the Ethics Committee in accordance with regulatory requirements. No formal interim analyses or independent audits are planned. Any significant amendments to the protocol will be reviewed by the Principal Investigator and submitted to the Ethics Committee for approval. Once approved, all relevant members of the study team will be informed, and the revised protocol will be stored with the local study documentation. Any deviations from the approved protocol will be documented using a breach report form, and the Clinical Trial Registry will be updated as required.

Study setting

Participants will be recruited through the Child and Adolescent Neuropsychiatry Unit at the General Hospital “Riuniti of Foggia”, community health clinics, and family associations supporting individuals with neurodevelopmental disorders. Initially, interested participants will receive detailed study information and undergo preliminary screening to assess eligibility based on age, confirmed ASD diagnosis, and absence of a personal history of seizures. Following this initial screening, eligible participants will be invited for an in-person assessment to further verify inclusion criteria. Those meeting the criteria will subsequently receive neurostimulation treatments as outpatients at the Department of Clinical and Experimental Medicine, University of Foggia. A detailed timeline of data collection is shown in Table 1. We used the SPIRIT checklist when writing our protocol (see Supplementary File 2) [27].

Eligibility criteria, sample size, and data collection

Participants will be eligible for inclusion in the study if they are between 7 and 18 years old and have a confirmed diagnosis of ASD based on DSM-5 criteria. Exclusion criteria for this study include any history of seizures, epilepsy, or repeated febrile seizures, as well as any severe or traumatic brain injury. Participants with comorbid neurological or genetic conditions that impact brain function or structure, such as brain tumors, fragile X syndrome, or tuberous sclerosis, will also be excluded. Additionally, those with known endocrine, cardiovascular, pulmonary, liver, kidney, or other significant medical diseases, or with any unstable medical condition, will not be eligible to participate. Participants will also be excluded if they are on an unstable medication regimen or using medications contraindicated for TMS. Vision or auditory impairments that could interfere with study participation will preclude eligibility. Participants will also be excluded if they demonstrate significant epileptiform activity on electroencephalogram (EEG), such as seizures or continuous epileptiform discharges. Furthermore, individuals with psychosis disorder and diagnosed chronic or acute inflammation or infection, or those unable to provide informed consent, will not be eligible for the study. To detect significant differences in outcomes between the active and sham groups with 80% statistical power and a significance level (α) of 0.05, the sample size calculation indicated that 20 participants per group (n = 40 total) would be required. We will use REDCap (Research Electronic Data Capture) as our clinical data management system. Personal information will be stored separately from study data, secured on password-protected systems, and pseudo-anonymized with unique numeric codes accessible only to authorized personnel.

Blinding and randomization

Participants, care providers, and clinical raters will be blinded to treatment assignment. Only the clinician who generates the treatment allocation and delivers the pulses will remain unblinded, without participating in any other study activities. Data analysis will be conducted independently by two statisticians who are not otherwise involved in the trial procedures. A stratified randomization approach with permuted blocks will be used. Clinical severity will be classified based on scores from the Autism Diagnostic Observation Schedule-2 (ADOS-2) and the Childhood Autism Rating Scale Second Edition (CARS-2). Participants will be stratified into mild-to-moderate and severe groups before randomization to ensure balanced allocation between treatment arms. This approach is intended to minimize baseline differences in symptom severity across groups. Randomization will be performed using a computer-generated allocation sequence (a function available in SAS software), with permuted blocks employed to prevent predictability of group assignments and maintain balance within each stratum.

Neuropsychological assessment and primary outcomes

A comprehensive neuropsychological assessment will be conducted using a battery of tests to confirm the ASD diagnosis and evaluate clinical severity. These assessments will be performed at baseline, post-treatment, and at a 1-month follow-up, representing the primary outcomes of the study. The ADOS-2 and Autism Diagnostic Interview-Revised (ADI-R) will be administered to evaluate social interaction, communication, play, and the imaginative use of materials across different age groups [28, 29]. The Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV) will be used to evaluate intellectual functioning in verbal children, whereas non-verbal children will be assessed with the Leiter International Performance Scale-Revised (Leiter-R) [30, 31]. To measure neuropsychological functions, particularly executive functions, the NEPSY Second Edition (NEPSY-II) will be used. The Movement Assessment Battery for Children Second Edition (MABC-2) will assess motor skills in everyday activities [32]. The CARS2 will be administered to evaluate autistic symptoms, and the Vineland Adaptive Behavior Scales Second Edition (Vineland-II) will assess a range of adaptive behaviors [33,34,35]. Additionally, parents will complete the Conners Third Edition (Conners 3) to report attention-deficit/hyperactivity disorder (ADHD) symptoms in their children, the Child Behavior Checklist for Ages 6–18 (CBCL/6–18) to identify behavioral and emotional problems, and the Social Communication Questionnaire (SCQ), a tool used to evaluate communication skills and social functioning [36,37,38]. The Children’s Depression Inventory 2 for parents (CDI-2) and the Multidimensional Anxiety Scale for Children (MASC) will also be used to assess depressive and anxiety symptoms [39, 40]. A detailed patient and medication history will be collected to assess any potential impact on the efficacy of rTMS.

Electroencephalogram recording

A standard EEG, following the international 10–20 system, will be recorded at each study time point: at baseline (T0), after rTMS treatment completion (T1), and at a 1-month post-treatment follow-up (T2). This procedure will identify pre-neurostimulation epileptic discharges that may exclude participants from the study and monitor any electrical changes induced by rTMS.

Intervention and adherence to protocol

The intervention will consist of 18 sessions of 2 Hz rTMS over 9 weeks, targeting the DLPFC. Stimulation intensity will be set at 90% of the motor threshold, delivering 180 pulses per session consisting of 9 trains of 20 pulses each, with a 20-s inter-train interval. The selection of 2 Hz frequency and 180 pulses per session was based on prior studies demonstrating that low-frequency stimulation over the DLPFC can reduce gamma activity and enhance executive function in individuals with ASD [4, 5, 19]. These parameters were selected to ensure both safety and potential clinical benefit in a pediatric population, consistent with previous trials that reported good tolerability and modulation of cortical excitability using low-frequency, low-dose protocols [4, 18]. The first six sessions will target the left DLPFC, the next six the right DLPFC, and the final six both hemispheres. rTMS will be administered using a Magstim R2 stimulator (Magstim, Whitland, UK) with a 70-mm figure-eight coil positioned at a 45° angle from the midline. Anatomical landmarks corresponding to EEG sites F3 and F4 (10–20 system) will be used to accurately target the DLPFC and to minimize discomfort in pediatric patients with ASD. Motor thresholds for each hemisphere will be established by incrementally raising machine output by 5% until a visible twitch in the first dorsal interosseous (FDI) muscle is observed in 2 out of 3 trials [41]. A sham group will undergo identical procedures without magnetic field application. The sham stimulation will replicate the auditory and tactile sensations of active rTMS by using the same coil orientation and clicking sounds. The coil will be positioned against the scalp at the same angle and location, and the stimulator will be activated to produce the characteristic clicking noise and slight vibration. This is to ensure that the sensory experience remains comparable between active and sham conditions, thereby supporting effective blinding. Throughout and immediately after each session, participants will be monitored for well-being, with any reported side effects documented and reviewed post-study. All sessions will be conducted under standardized conditions, with participants seated comfortably in an armchair in a quiet room, and their elbows positioned at a 90° flexion angle.

All rTMS sessions will be scheduled at consistent times and on the same days to establish a routine for participants and their parents or caregivers. Reminders via phone calls or text messages will be sent to parents or caregivers before each session. Missed sessions will be promptly rescheduled within the same week whenever possible. Study staff will educate families on the importance of session attendance and protocol compliance. Reasons for missed sessions will be documented, and efforts will be made to re-engage participants. The rTMS clinician will complete a checklist at each session to confirm that all protocol steps (e.g., stimulation parameters, coil positioning) are followed.

Biochemical measures and secondary outcomes

Biochemical measures will be assessed as secondary outcomes to provide a more comprehensive understanding of the effects of rTMS on ASD. Tryptophan metabolites, including 3-hydroxykynurenine (3-HKYN), KYNA, and QUIN, will be measured in urine using

high-performance liquid chromatography with electrochemical detection (HPLC-ECD) (Ultimate ECD, Dionex Scientific, Milan, Italy) [42]. HPLC-ECD will be also used to measure systemic levels of neurotransmitters, such as glutamate, GABA, serotonin, and dopamine, in urine samples [43]. To measure circulating BDNF, serum samples will be collected, and an enzyme-linked immunosorbent assay (ELISA) (DBA Italia, Segrate, Italy) will be performed [44]. Additionally, BDNF gene polymorphism (Val66Met) will be genotyped using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR–RFLP). This approach amplifies the BDNF gene region containing the Val66Met variant, followed by restriction enzyme digestion to distinguish the Val and Met alleles based on fragment length analysis [45]. Inflammatory mediators, including IL-6, IL-10, IL-1β, TNF-α, and CRP, will be measured in serum using ELISA [46].

Follow-up

A follow-up visit will be scheduled 1 month after the final rTMS session to evaluate the persistence of rTMS effects and the need for possible booster sessions. Accordingly, neuropsychological assessments and biochemical sampling will be performed again at the follow-up visit. To enhance the response rate, the study coordinator will request contact information from both parents.

Safety

Before the enrollment, participants will undergo comprehensive screening to confirm they meet eligibility and rTMS safety criteria. Additionally, a qualified physician will review medical history and perform a general physical examination to confirm the absence of risk factors. After enrollment, a standard clinical EEG, reviewed by a neurologist, will be conducted to exclude participants with epileptiform discharges. To ensure safety, rTMS will be administered by a trained neuropsychiatrist equipped to promptly manage any seizures or adverse events. A stimulation intensity of 90% of the motor threshold will be used for ASD participants to minimize seizure risk. Before each rTMS session, participants will be asked about their current health status and any adverse events experienced since the previous session. All adverse events will be documented and reported. In the case of a serious adverse event, treatment will be suspended.

Statistical methods

Statistical analyses will be performed using SPSS (IBM Corp., Armonk, NY). Primary analyses will focus on evaluating differences between the active and sham rTMS groups across three time points: baseline (T0), immediately post-treatment (T1), and 1-month follow-up (T2). Changes in neuropsychological and clinical measures across time points will be assessed using repeated-measures analysis of variance (ANOVA), with group (active vs. sham) as the between-subjects factor. If assumptions of normality are not met, the Wilcoxon signed-rank test will be applied to evaluate within-group differences over time, while the Mann–Whitney U test will be used to compare between-group differences at each time point. Biomarker levels will be compared using generalized linear models to account for potential covariates, including age and baseline severity. Chi-square tests will evaluate categorical variables, and t-tests will be applied for continuous variables where applicable. Spearman’s rank or Pearson correlation coefficients, depending on data distribution, will be employed to examine relationships between neuropsychological scores and biochemical measures. All statistical tests will be two-sided, with p-values below 0.05 considered statistically significant.