Background: Herbs originating from the Aconitum L. (Ranunculaceae), such as Aconitum carmichaelii Debeaux. (Wutou), Aconitum pendulum Busch. (Tiebangchui), and Aconitum kusnezoffii Reichb. (Caowu), etc. are highly valued for their medicinal properties. The roots and tubers of these herbs are commonly used to treat an array of ailments, including joint pain and tumors. The alkaloids present in them are the primary active components, with aconitine being the most notable. Aconitine has gained attention for its exceptional anti-inflammatory and analgesic properties, as well as its potential as an anti-tumor and cardiotonic agent. However, the exact process through which aconitine hinders the growth of cancerous cells and triggers their programmed cell death remains unclear. Therefore, we have undertaken a comprehensive systematic review and meta-analysis of the current research on the potential antitumor properties of aconitine.Methods: We conducted a thorough search of relevant preclinical studies in databases including PubMed, Web of Science, VIP, WanFang Data, CNKI, Embase, Cochrane Library, and National Center for Biotechnology Information (NCBI). The search was conducted up until 15 September 2022, and the data were statistically analyzed using RevMan 5.4 software. The number of tumor cell value-added, tumor cell apoptosis rate, thymus index (TI), and Bcl-2 gene expression level were the main indicators to be analyzed.Results: After applying the final inclusion criteria, a total of thirty-seven studies, comprising both in vivo and in vitro research were analyzed. The results showed that treatment with aconitine led to a significant reduction in tumor cell proliferation, a noteworthy increase in the rate of apoptosis among tumor cells, a decrease in the thymus index, and a reduction in the expression level of Bcl-2. These results suggested that aconitine could inhibit the proliferation, invasion, and migration abilities of tumor cells by regulating Bcl-2 etc., thereby enhancing the anti-tumor effects.Conclusion: In summary, our present study demonstrated that aconitine effectively reduced tumor size and volume, indicating a strong anti-tumor effect. Additionally, aconitine could increase the expression levels of caspase-3, Bax and other targets. Mechanistically, it may regulate the expression levels of Bax and Bcl-2 through the NF-κB signaling pathway, ultimately inhibiting tumor cell proliferation through autophagy. A malignant tumor refers to a vast array of diseases that are characterized by the loss of normal cellular regulation, uncontrolled growth, abnormal differentiation, local tissue infiltration, and distant transplantation . MalignaAconitum L., including Aconitum carmichaelii Debeaux. (Wutou), Aconitum brachypodum Diels. (Xueshangyizhihao), Aconitum coreanum (H. Lév.) Raipaics. (Huanghuawutou), Aconitum kusnezoffii Reichb. (Caowu), and Aconitum pendulum Busch. (Tiebangchui), etc. are well-known Chinese herbal medicines. It is reported to be widely used in many traditional medical systems, including Chinese, Tibetan, Mongolian, and Indian medicine. Modern pharmacological research has confirmed their analgesic and anti-tumor effects, which has sparked interest among scholars in the field of oncology (34H47NO11) is a C19-diterpenoid alkaloid that possesses good anti-tumor effects and is the main medicinal ingredient in Aconitum medicinal materials  and followed the PRISMA guidelines.In this subject paper, we utilized various computerized search databases such as WanFang Data, CNKI, VIP, PubMed, Embase, Web of Science, and Cochrane Library. The search period was limited to the date of creation up to 15 September 2022. Additionally, to ensure that we did not miss any relevant articles, we conducted a manual search of the retrieved articles.The Chinese literature search was conducted through the following search strategy: ① “aconitine” or “aconitine alkaloid”; ② “anti-cancer” or “anti-tumor” or “anti-proliferation” or “anti-proliferative” or “anti-proliferative activity” or “inhibition of cell proliferation”; ③ “cancer cell” or “tumor cell”; ④ including ①②③ and other subject words or free words.The search strategy for Chinese literature involved the following steps: ①, searching for keywords “aconitine” or “aconitine alkaloid”; ②, looking for terms such as “anti-cancer” or “anti-tumor” or “anti-proliferation” or “anti-proliferative” or “anti-proliferative activity” or “inhibition of cell proliferation”; ③, searching for phrases like “cancer cell” or “tumor cell”; ④, including ①②③ along with other relevant subject words or free words.To search for English literature on the topic, the following strategy was employed: ①, Keywords such as “Aconitine” or “Aconitum alkaloid” or “Aconitine alkaloids” were used; ②, Terms such as “Anticancer” or “Antitumor” or “Antiproliferative” “Anti-proliferation” or “Antiproliferative activity” were included; ③, Phrases like “Cancer cell” or “Tumor cell” were also added; ④, All the above terms were combined using “OR” and “AND” to form a comprehensive search group. In addition, other relevant subject words and free words were included.These were the techniques we used to conduct our literature searches: [“aconitine” (MeSH Terms) OR “aconitine” (All Fields)] OR [“aconitum” (MeSH Terms) OR “aconitum” (All Fields)] AND [“alkaloids” (MeSH Terms) OR “alkaloids” (All Fields) OR “alkaloid” (All Fields)] OR [“aconitine” (MeSH Terms) OR “aconitine” (All Fields)] AND [“alkaloids” (MeSH Terms) OR “alkaloids” (All Fields)] AND [Anticancer (All Fields) OR Antitumor (All Fields) OR Antiproliferative (All Fields) OR Anti-proliferation (All Fields)] OR [Antiproliferative (All Fields)] AND [“motor activity” (MeSH Terms)] OR [“motor” (All Fields) AND “activity” (All Fields)] OR [“motor activity” (All Fields) OR “activity” (All Fields)] AND [“cancer” (All Fields) AND “cell” (All Fields)] OR [“cancer cell” (All Fields)] OR [“tumour” (All Fields) OR “neoplasms” (MeSH Terms) OR “neoplasms” (All Fields) OR “tumor” (All Fields)] AND [“cells” (MeSH Terms) OR “cells” (All Fields) OR “cell” (All Fields)].1) Study: Aconitine inhibits malignant tumor cell proliferation and promotes tumor cell apoptosis related article. 2) The experimental group received aconitine monotherapy without regard to treatment method, dose and frequency. 3) The control group was given only saline, pure water or no adjuvant intervention. 4) Outcome indicators: the main outcome indicators included tumor weight, tumor cell inhibition rate (IR), tumor cell apoptosis rate, thymus index (TI), and degree of apoptotic invasion, which encompassed at least one of the above indicators.1) The target disease was not malignancy, cancer, etc.; 2) There was no control group; 3) The trial group received a combination of aconitine; 4) Duplicate published literature; 5) Studies were clinical studies, case reports, clinical trials, reviews, conference papers, abstracts, reviews, and patent results; 6) Unpublished dissertations; and 7) Literature for which data could not be extracted.Two investigators conducted literature screening and data extraction independently, utilizing Endnote and adhering to the aforementioned inclusion and exclusion criteria. The extracted data will be organized using Excel software and cross-checked for accuracy. In the event of any discrepancies, a joint decision will be made after consulting with a third researcher. Further details regarding the data extraction process can be found below: 1) The first author and year of publication of the literature; 2) Individual data of the experimental study, such as animal species, sex, body weight, and individual comparison samples; 3) The type of anesthesia; 4) The intervention characteristics of the treatment and control groups, including drug dosage form, dose, treatment method, and frequency of administration; and 5) The mean, standard deviation, and between-group differences of measurements and corresponding data.Two authors conducted independent assessments of the methodological quality of the studies included in this paper. The assessments were based on the CAMARADES list, which provides a collaborative approach to meta-analysis and review of animal data from experimental studies. The list comprises 10 items : ①PublicI2, and when the results of the heterogeneity test were not statistically significant , a fixed-effects model was employed for meta-analysis, However, if I2 > 50%, a heterogeneity test was required to identify the cause of heterogeneity. If heterogeneity remained above 50% after sensitivity analysis or subgroup analysis, only a description of the results was required. The results were considered statistically significant with a p-value of p < 0.05. Meta-analysis test level was a = 0.05, and publication bias was expressed using funnel plots.Meta-analysis was performed by using Review Manager 5.4 software. Odds ratios (OR) were used to express count data as statistical effect sizes, while mean difference (MD) was used to express continuous variables. To ensure accuracy, all effect sizes were accompanied by a 95% confidence interval (CI). Heterogeneity was evaluated using Higgins Based on the search method described above, a total of 2581 documents were initially searched. After removing 407 duplicate documents, 2174 articles remained. Of these, 1606 articles were excluded as they were reviews, experiences, conferences, patents, or results, leaving 568 articles after the initial screening. After reviewing the titles and abstracts of these articles, 450 papers were found to have inconsistent contents and were excluded, resulting in a final selection of 118 papers. After reading the full text, we excluded several papers. Specifically, we excluded 21 papers that were reviewed, 36 papers that involved combined drug interventions in both the treatment and control groups, 21 papers from which we were unable to extract experimental data, 2 clinical papers, 8 papers that were tested with drugs other than aconitine alkaloids, and 30 papers that studied diseases other than antitumor. Ultimately, we included 37 literatures for analysis. The screening process is shown in The subject paper included thirty-seven preclinical studies, which could be classified into five animal experiments, twenty-four cellular experiments, and eight experiments that involved both animal and cellular testing. The animal experiments involved a combination of male and female animals in four studies, while eight studies used only male animals and one study used only female animals. Additionally, thirteen animal studies provided information on the body weight of the animals. Eight studies did not report the method of animal execution, while four studies utilized decerebrate execution and one study used 0.1 mL of 1% pentobarbital solution for anesthetic execution. The dosing time ranged from a minimum of 5 days to a maximum of 26 weeks. In terms of cellular experiments, fifteen studies mentioned the cell inhibition rate or cell proliferation inhibition rate. Sixteen studies reported on apoptosis rate, while six studies focused on detecting rate OD or absorbance A value. Additionally, five studies provided insight into different cell cycle numbers, while six studies explored cell invasion ability, cell invasion inhibition rate, or cell invasion number. Furthermore, twelve studies delved into the expression levels of proteins such as p21-Ras, p38MAPK, p53, Bcl-2, Bax, and others. The detailed characteristics of the included studies are shown in The CAMARADES evaluation form was utilized to assess the quality of the literature included in the 37 preclinical trials, as presented in Tau2 = 0.28, Chi2 = 73.07, df = 6, I2 = 92%, p < 0.00001), so a random-effects model was utilized. The findings indicated a significant difference in tumor weight between the treatment and control groups  with a 95% confidence interval of . The results were shown in Seven studies were analyzed to compare the change in tumor weight between the control and experimental groups. Heterogeneity was observed between the , requiring the use of a random effects model. The results indicated a significant difference between the control and experimental groups  with a 95% confidence interval of . This indicated that aconitine could reduce the number of tumor cell proliferation in mice. The results were shown in Two studies were analyzed to compare changes in cell proliferation numbers between control and experimental groups. Heterogeneity was observed between trials , a random-effects model was utilized. The results showed a statistically significant difference in thymic index between the treatment and control groups , with a 95% confidence interval of . The results of this study showed that aconitine could reduce the thymic index in mice. The results were shown in A total of two studies were included to compare the change in thymic index between the control and experimental groups. Due to heterogeneity between trials , so a random-effects model was used. T The results indicated that there was no significant difference in the number of metastatic lesions between the treatment and control groups . Therefore, it cannot be concluded that aconitine has the ability to reduce the number of metastatic lesions in mice. The results were shown in A total of two studies were included to compare the changes in the number of metastatic lesions between the control and experimental groups, and there was heterogeneity between the trials , so a random-effects model was used. The results indicated a significant difference in apoptosis rate between the treatment and control groups  with a 95% confidence interval of . The results of this study showed that aconitine could increase the apoptosis rate of tumor cells. The results were shown in A total of seven studies were included to compare apoptosis rates between control and experimental groups, and there was heterogeneity between trials , so a random effects model was used. The results showed a statistically significant difference between the treatment and control groups  with a 95% confidence interval of . The results of this study showed a decrease in cell proliferation OD. The results were shown in A total of seven studies were included to compare the cell proliferation OD between control and experimental groups, with heterogeneity between trials , so a random effects model was used. The results revealed a statistically significant difference between the treatment and control groups  with a 95% confidence interval of . The results of this study showed that aconitine reduced the expression of Bcl-2. The results were shown in A total of three studies were included to compare the anti-cycloguanine peptides between the control and experimental groups. There was some heterogeneity between the trials  are a specialized type of T cell that secretes various immune-related cytokines. Along with natural killer cells, they form a crucial line of defense for the body’s antiviral and antitumor immunity . CD8+ T response .+ T cells and CD4+ T cells are both types of CTL and are believed to have a dominant role in producing an effective anti-tumor response (Cytotoxic T lymphocytes (CTL) are a specialized type of T cell that secretes various immune-related cytokines. Along with natural killer cells, they form a crucial line of defense for the body’s antiviral and antitumor immunity . CD8+ T response .+ T cells are known to have the ability to directly eliminate tumor cells, making them the preferred immune cells for targeting tumors , the NF-κB signaling pathway begins to activate . This acThe NF-κB pathway has a number of known activators, including TNF-α , interleThe NF-κB pathway’s anti-apoptotic mechanism involves inhibiting the activation of caspase-8, which in turn inhibits downstream caspase-3. This inhibition is achieved by regulating the expression of TRAF1, TRAF2, c-IAP1, and c-IAP2. Inclusion studies indicate that aconitine is likely to promote the expression of the Bax gene and suppress that of the Bcl-2 gene by regulating the NF-κB signaling pathway. Additionally, aconitine regulates the expression of cell cycle proteins, thereby inhibiting tumor cell proliferation and inducing apoptosis. The anti-apoptotic mechanism of NF-κB is to inhibit the activation of caspase-8  and thus+ channels in the thymus, promoting thymic T cell development, while also inhibiting Bcl-2 gene expression and activating the downstream gene Caspase-3 to promote tumor cell apoptosis through the regulation of the NF-κB signaling pathway.Although aconitine is highly toxic, it has a remarkable anti-tumor effect. Its mechanism of action is complex, but it inhibits tumor cell proliferation and induces apoptosis. Aconitine activates voltage-dependent Na50) of 0.2702 ± 0.002 mg/kg in mice, respectively (+ channels in the thymus and promoting thymic T cell development on the one hand. on the other hand, it inhibits Bcl-2 expression and activates the downstream Caspase-3 to promote tumor cell apoptosis by regulating the NF-κB signaling pathway. However, the precise mechanism of aconitine remains unexplored in both preclinical and clinical trials, whether in vivo or in vitro. At the same time, the challenge of regulating the dosage of aconitine in isolation during clinical trials has led to its frequent use as part of a combination drug in multidrug resistance trials for certain drug-resistant medications. But there are limited in vitro and in vivo preclinical and clinical studies on this subject, making it a key area of investigation for advancing the clinical application of aconitine.Studies have shown that aconitine is highly toxic, with lethal doses (LDectively . Althoug


Fusarium oxysporum f. sp. Cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. Malaccensis segregating populations, segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on chromosome 3 of ‘DH-Pahang’ reference assembly v4. Within this region, there was a cluster of pattern recognition receptors, namely leucine-rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell-wall-associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins, positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant progenies but not in the susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent ‘Ma850’ and a susceptible line ‘Ma848’, to show that the STR4 resistance co-segregated with marker ‘28820’ at this locus. Finally, an informative SNP marker 29730 allowed the locus-specific resistance to be assessed in a collection of diploid and polyploid banana plants. Of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4-resistant, such as ‘Pahang’, ‘SH-3362’, ‘SH-3217’, ‘Ma-ITC0250’, and ‘DH-Pahang/CIRAD 930’. Additional screening in the International Institute for Tropical Agriculture’s collection suggests that the dominant allele is common among the elite ‘Matooke’ NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland bananas. Fine mapping and candidate gene identification will allow characterization of molecular mechanisms underlying the TR4 resistance. The markers developed in this study can now aid the marker-assisted selection of TR4 resistance in breeding programs around the world.Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to  Musa spp.) are an important horticulture crop, typically consumed as a fruit or staple food, and they are cultivated in the tropical and subtropical regions around the world. Musa spp. were domesticated in Southeast Asia and Melanesia, and hybridisation involving mainly A (Musa acuminata) and B  genome progenitors gave rise to most of the domesticated forms of the dessert and plantain bananas we see today [Musa acuminata have been divided into multiple subspecies [Bananas . Foc can be classified into a race structure, reflecting its banana host range [Foc race 1 was the cause of the pandemic that decimated the triploid cultivar ‘Gros Michel’ (genome AAA) during the last century. Its replacement, the ‘Cavendish’ banana, is resistant to Foc race 1. Cavendish bananas are now the dominant cultivar on the market, accounting for more than 40% of the 124 M tonnes of world banana production in 2021 [Fusarium wilt of banana (FWB), also known as Panama disease, is one of the most devastating diseases affecting banana plants. The global epidemics owing to FWB have put major constraints on banana production both historically and at the present time ,9. The cst range ,11,12,13 in 2021 , with ex in 2021 .During the 1990s, a previously unknown race, the tropical race 4 (TR4) of FWB, emerged and decimated Cavendish plantations around the world ,17. AccoFoc infects banana plants through the roots then travels through the vascular vessels to colonise the rhizome and the pseudostem of susceptible plants [Foc is disseminated in infected soil, it can remain in the soil for decades, surviving as chlamydospores on infected planting material or as endophytes on alternative weed hosts and spreading through the movement of contaminated water and soil [Tricoderma spp. or endophytic F. oxysporum spp. [e plants ,34. Sympand soil ,36. Diseand soil , providiand soil  and biocrum spp. ,38,39.Foc provides a long-term solution for the management of the disease. Foc race 4 type resistance has been detected in both wild and cultivated banana plants [M. acuminata ssp. malaccensis ‘Pahang’, ‘DH-Pahang’, M. acuminata ssp. burmannica ‘Calcutta 4’, M. itinerans, cv. ‘Tuu Gia’, and cv. ‘Rose’, are highly resistant to Foc TR4 [Host genetic resistance to a plants ,43,44,45 Foc TR4 ,45,46. I Foc TR4 ,43,45. I Foc TR4 ,43,45.Forward genetic approaches have led to the identification of genes controlling plant yield and development, as well as biotic and abiotic stress tolerance . GeneticM. acuminata ssp. banksii- and M. acuminata ssp. malaccensis-derived populations. However, these markers are not easily transferable to other populations, and large segregation distortion has been observed [Musa [Musa spp. [In banana, forward genetics have not been performed frequently due to experimental constraints associated with sterility, polyploidy, long life cycles in population development, and phenotypic assessments . Linkageobserved . More reed [Musa . DArTsequsa spp. ,55,56.Musa spp. contained a diploid genome and were taxonomically characterised as Musa acuminata ssp. malaccensis [M. acuminata ssp. malaccensis lines were resistant to both STR4 and TR4 [M. acuminata ssp. malaccensis origin [M. acuminata ssp. malaccensis reference genome [Previously, we used flow cytometry and simple sequence repeat genotyping to show that wild lines of accensis . These M and TR4 ,58, and  and TR4 . This QT and TR4 . Genome s origin , making e genome .2 population with SNP-based cleaved amplified polymorphism sequence (CAPS) markers. Individuals carrying recombination events were tested against both STR4 and TR4 strains to define and limit the candidate region. One marker carried an informative SNP that allowed chromosome 3-specific resistance to be assayed in 132 Musa accessions, including the core M. acuminata ssp. malaccensis collection from the International Musa Germplasm Transit Centre (ITC), as well as a comprehensive collection of diploid and polyploid genotypes from the International Institute for Tropical Agriculture (IITA) in Nigeria and Uganda. The validation of this marker will allow marker-assisted selection of TR4 and STR4 resistance to be deployed in breeding programs around the world.In this study, we performed genetic mapping in the chromosome 3 QTL region by screening a self-derived FM. acuminata ssp. malaccensis F1 parental lines were derived from two independent progenitors  and three STR4-susceptible  genitors . From eaeveloped B. ‘Populeveloped B. A tota2 individuals of Population 1. The genetic distance in centiMorgan (cM) was calculated as the number of progenies carrying a cross-over event between a pair of adjacent markers over the total number of individuals  (Foc-STR4-susceptible phenotypes (2) values of marker–trait association are the highest at markers 28820 and 29460 (p = 0.05), confirming that they are positioned closest to the trait locus  B. Among enotypes B. The suenotypes A,B. Thisenotypes . Furtherenotypes A,B, althit locus A.M. acuminata ssp. malaccensis was not optimally detected at the current inoculum dosage. However, all susceptible recombinants except 852-7, 852-47, and 1 showed a disease incidence  screened per genotype) between 20–100%. All critical recombinant phenotypes (except 852-7) were correctly associated with the direction of the trait locus between 28420 and 29590 .TR4 phenotyping of a subset of critical recombinants produced a similar result C. The rhnd 29590 A,C. The  p = 0.1 A. The phA set of 24 Population 1 progenies that are homozygous for the resistant ‘A’ or susceptible ‘B’ for all eleven markers across this region were used to perform a transcriptome analysis with RNAseq. The phenotype of each of these lines was confirmed in a pot trial prior to the start of this experiment. The experiment was designed to identify a narrow transcriptome response that is specifically controlled by the resistance locus in this region. Genetic effects unlinked to this locus are accounted for by the segregation of these genes in the genetic background.p-adj. < 0.05) that were associated with plant defense under the ontology of ‘Biological Process’, namely ‘defense response to bacterium’  and ‘defense response to fungus’  (p-adj. < 0.05).Our previous study identified multiple classes of R genes present in the candidate region . Differe5 genes) . Under ‘p-adj. < 0.05 (p-adj. < 0.05) (p-adj. < 0.01) than S transcripts at 1 dpi  C. The trat 1 dpi D. The reat 7 dpi E. The 31at 7 dpi F. Its trat 7 dpi G. Again,at 7 dpi H, whereaThe haplotype analysis across the QTL region showed that the marker loci were all heterozygous in the R parents and were susceptible ‘B’ haplotype interrupted by heterozygous segments in the S parents A. The ca2 progenies of the ‘Ma848’ × ‘Ma850’ cross were screened for STR4 resistance , and 35 individuals were susceptible (mean RDI ≥ 4) .To validate the segregation of resistance observed in Population 1, 38 Fsistance B. There sistance C. DecoupRDI ≥ 4) D, with gMusa acuminata ssp. malaccensis parents, ‘DH-Pahang’, ‘Pahang’, ‘SH3362’, ‘FHIA25’, ‘Pisang Jari Buaya’, and ‘Calcutta 4’) that are known to carry STR4/TR4 resistance. Of all the markers tested, only one marker, 29730, showed an association with STR4/TR4 resistance in a subset of these genotypes. All the other SNPs interrogated were not correlated with the resistance/susceptibility of accessions outside of the mapping populations. This marker, along with A-genome (M. acuminata)-specific primers for 29730-A were subsequently developed  and 46 accessions from the IITA’s M. acuminata ssp. banksii collection (Nigeria). Of the 11 ‘Matooke’ tetraploid parents screened, two of them, ‘1438K-1’ and ‘376K-7’, were positive for the resistant band  was related to the wheat LRK10 gene [By using transcriptome sequencing on S or R progenies carrying contrasting haplotypes in the QTL region, candidate R genes underlying resistance were identified. Segregant analysis is a powerful approach when combined with the positional information from genetic mapping. Firstly, the candidate region was confirmed in Population 1. The marker haplotype in the susceptible parents and the segregation of K10 gene . TranscrK10 gene ,73 and hK10 gene ,75, and K10 gene .Trichoderma viride [Verticillium dahlia was recognised by the Nicotiana benthamiana LRR RLP NbEIX2 [N. benthamiana.The genetic Interval closest to the STR4 resistance locus is between 28820 and 29590. It is not well-defined at this stage. Only two individuals were identified with crossovers between these markers. More recombinants are needed to narrow this interval more precisely. In the larger region between markers 28840 and 29590, multiple recombinants consistently confirmed the direction of the trait locus on either side. Although one critical recombinant (852-7) did not produce any symptoms in the TR4 screening, the phenotypic data were generally concordant with the genetic interval defined for both STR4- and TR4-resistant loci. Within this interval, there was a cluster of receptor-like kinases (LRR XII subfamily) and receptor-like proteins (LRR RLP subfamily) positioned in an interspersed arrangement . They, ra viride . SimilarP NbEIX2 , inducinSeptoria tritici blotch and leaf rust in wheat [Pseudomonas syringae [Pythium irregulare [Xanthomonas species to trigger immune responses [A gene encoding a cysteine-rich protein kinase was also strongly upregulated during the onset of infection in the R but not in the S genotypes. Cysteine-rich protein kinases contained DUF domains and a kinase domain. Such genes have been found to confer resistance against in wheat ,82. Oversyringae . In addisyringae . The LRRsyringae , and PEPregulare ,87. In responses .Overall, there are multiple resistance genes differentially expressed between the S and R banana progenies with similar temporal expression profiles. All of them are indicative of a rapid response in the induction of resistance gene transcripts at the onset of STR4 infection. This suggests that these genes may act in close proximity to one another or even belong to the same gene network. Co-expression gene networks will be constructed from RNA sequencing data to identify co-expression modules. This information can then be integrated with the QTL region to characterize the candidate genes .Musa are highly heterozygous, which render it challenging for genetics to be undertaken. The resistance source identified in this population was dominant, which is consistent with the mode of inheritance of a race 1 and, to a lesser extent, TR4-resistant QTLs located on chromosome 10 of a different Musa acuminata ssp. malaccensis [In this study, we demonstrated that SNP loci/trait associations can produce markers useful for marker-assisted selection. Unlike traditional bi-parental mapping, the wild subspecies of accensis . The domaccensis .M. acuminata ssp. malaccensis origin . Despite being resistant to TR4, ‘Pisang Jari Buaya’ was a negative in our marker screen. The parentage of ‘SH3217’ can be further traced back to a cross between ‘SH2095’ and ‘SH2766’. ‘SH2095’ was derived from a cross between ‘Sinwobogi’ (AA) and ‘Tjau Lagada’ (AA), whereas ‘SH2766’ was derived from ‘Tjau Lagada’ (AA) and the progeny of a cross between M. acuminata ssp. malaccensis and ‘Guyod’ (AA) ‘https://www.promusa.org/NARITA+16’ (accessed on 12 March 2023). Therefore, the source of resistance potentially can be traced back to a M. acuminata ssp. malaccensis origin, although validation is not possible without these progenitors or their DNA. ‘SH3362’ and its progenitor ‘SH3217’ were the male parents of 13 hybrids in the IITA collection  parents ‘Ma850’, ‘Ma851’, and ‘Ma852’ segregated for Foc-STR4 and Foc-TR4 resistance [Foc-STR4 , which was derived from an inter-cross between ‘Ma850’ and ‘Ma848’.Three sistance ,58, wherFoc-STR4 A. Three wo lines B. SegregFoc-STR4 screening, three monoconidial VCG0120 isolates  from the Queensland Plant Pathology Herbarium were used as a combined inoculum at the University of Queensland. For the Foc-TR4 screening, a VCG01213/16 isolate from the culture collection of Stellenbosch University’s Department of Plant Pathology was used.For the Foc-STR4 pot trials were conducted in temperature-controlled glasshouses at the University of Queensland, St Lucia campus, QLD, Australia. The temperatures were controlled at 26 °C day/22 °C night for the entire duration of the experiments. Humidity was maintained at 60%. The amount of 50mL of 2.0 × 106 conidia/mL solution was poured directly into potted plants with a stem height of 30 cm, followed by spreading a layer of Foc-STR4-infested millet (20–30 g) on the surface of the soil. Protocols for preparing Foc-infested millet and conidia suspensions were previously described [escribed ,99. The escribed .Foc-TR4 pot trials were performed in a quarantined glasshouse at the University of Stellenbosch. Plants were hardened off for 2–3 months before the screening. The experimental setup for the pot trial was as previously described [Foc-TR4-inoculated Williams, respectively.escribed . A milleescribed . The poshttps://banana-genome-hub.southgreen.fr’ (accessed on 5 March 2023) were used to identify genes and SNPs suitable for marker development. Restriction enzyme-cutting sites covering the SNP site were identified using ‘NEB cutter v2.0’ ‘https://nc2.neb.com/NEBcutter2/’ (accessed on 5 March 2023). Enzymes that had multiple restriction sites within a 400 bp region flanking the SNP on each side were avoided. Primers flanking a 344–795 bp amplicon were designed using ‘Primer 3’ [https://sg.idtdna.com/calc/analyzer’ (accessed on 5 March 2023).SNPs were initially identified using a sequencing approach. The 100 bp paired-end sequencing was performed using the Illumina Genome Analyzer IIx platform  at the Australian Genome Research Facility, VIC, Australia, to produce 10x coverage for individually sequenced S and R libraries. There were 6 S libraries prepared, consisting of each of the 3 susceptible parents, ‘Ma845’, ‘Ma846’, and ‘Ma848’, as well as a pool of 34 susceptible progenies of ‘Ma845’, a pool of 3 susceptible progenies of ‘Ma851’, and a pool of 8 susceptible progenies of ‘Ma852’. Six R libraries were prepared. They consisted of each of the 3 resistant parents, ‘Ma850’, ‘Ma851’, and ‘Ma852’, and 3 DNA pools of 11, 17, and 24 resistant progenies (either homozygous or heterozygous for resistance), respectively, derived from ‘Ma850’, ‘Ma851’, and ‘Ma852’. Data generated from individual libraries were used to align to ‘DH Pahang’ v1 using SOAPaligner v2.21 , and SNPrimer 3’  and furtw/v) agarose gel to check for band shearing and/or contamination with either RNA or polysaccharide.DNA extraction was performed using a hexadecyltrimethylammonium bromide (CTAB)-based method , with moPCR was performed using 80–100 ng of DNA template and Dreamtaq . Running conditions were set according to the manufacturer’s recommendations. The primers and the corresponding annealing temperatures were optimized . Forty cFoc spore suspension was used to inoculate the plants [n = 3) were harvested at 0, 1, 3, and 7 days post-inoculation (dpi). Samples were snap-frozen in liquid nitrogen and then ground to powder using a mortar and pestle. SpectrumTM Plant Total RNA kit  was used to extract RNA. Here, 24 cDNA libraries corresponding to the R and S progenies harvested at the 4 time points were prepared and then sequenced using the Hiseq 4000 platform , generating approximately 48 Mb of 150 bp paired-end reads for each sample. Adaptor sequences and low-quality reads were filtered out using ‘Fastp’ [p-values were adjusted (p-adj.) to have a false discovery rate (FDR) cut-off of 0.05.A transcriptome study was performed by using 12 R and 12 S progenies from Population 1. These progenies were tested against STR4, and their resistance/susceptible phenotypes were confirmed prior to the start of this experiment. A root-dipping method using e plants , and who ‘Fastp’ . Clean p ‘Fastp’ . Non-nor ‘Fastp’  and then ‘Fastp’ . This va ‘Fastp’ . Multipl ‘Fastp’ . The p-v2) values on the phenotype were estimated on the basis of the fixed-effects model and reflected the phenotypic variation explained at each marker-defined locus. To analyze the STR4 and TR4 phenotypes of the recombinants, Waller–Duncan’s multiple range testing was performed as a post hoc test to separate the means of the recombinants into subsets by least significant difference (LSD). Recombinants with n < 2 were excluded from the analysis. The harmonic mean sample size was estimated and used to account for the unequal variances associated with the uneven sample sizes (n) of the recombinants. The type 1/type 2 error seriousness ratio (k-ratio) was set to 100 (α = 0.05).The statistical software SPSS v28.0.1.0 (142)  was used to perform the statistical analysis described in this study. One-way ANOVA was performed in a pair-wise manner, with phenotype set as a dependent variable and marker-defined genotypes (B/H) as factors, to compare the means of STR4 and TR4 sensitivity at these loci. Any ‘A’ alleles were considered as ‘H’ for the purpose of statistical analysis, as resistance is completely dominant over susceptibility at this locus. The eta-squared (ηMusa accessions. The availability of molecular makers closely linked to the resistance locus can now facilitate the rapid screening of potentially TR4-resistant genotypes and thereby reduce the generation time required for phenotypic and field trials. However, this marker can detect resistances originating from M. acuminata ssp. malaccensis at this locus only. Given the prevalence of TR4 now threatening the entire banana industry worldwide, identification of candidate receptors, such as proteins and kinases with strong transcriptional evidence linking them to resistance at this locus, provides the first step towards molecular dissection of resistance mediated by these R genes in banana.This study is the first-ever report of marker-assisted selection of STR4- and TR4-resistant


N = 245 dyads). We examine the role of depressive symptoms of incoming students and their roommates on their GPAs and class withdrawals (provided by university registrars) at the end of the Fall and Spring semesters. We test contagion between the roommates on both academic outcomes and depressive symptoms over time. Finally, we examine the moderating role of relationship closeness. Whereas students’ own initial levels of depressive symptoms predicted their own lower GPA and more course withdrawals, they did not directly predict the academic outcomes of their roommates. For roommates who form close relationships, there was evidence of contagion of both GPAs and depressive symptoms at the end of Fall and Spring semesters. Finally, a longitudinal path model showed that as depressive symptoms spread from the student to their roommate, the roommate’s GPA decreased. The current work sheds light on a common college experience with implications for the role of interventions to increase the academic and mental health of college students.Rising rates of depression among adolescents raise many questions about the role of depressive symptoms in academic outcomes for college students and their roommates. In the current longitudinal study, we follow previously unacquainted roommate dyads over their first year in college ( Students arriving at college with depressive symptoms tend to achieve lower grade point averages (GPAs) and are more likely to drop out during their first two years at school , 2. LessBoth the transition to college and the onset of several mental health disorders occur in late adolescence . In receResearch on the relationship between depressive symptoms and academic performance in college students finds that increased depression levels are related to lower grades –10. For Work on social contagion  has showA separate body of previous research has examined social contagion of depression in roommate relationships. A survey of roommate dyads found that students’ initial levels of depressive symptoms predicted depressive symptoms in their roommates three weeks later . In a loIt may be that the extent to which roommates’ depressive symptoms or grades become similar over time depends on the closeness of the roommate relationship. Whereas previous work has found associations of depression levels between close adolescent friends  and witIn the current study, we examined first year roommate dyads–all of whom were unknown to each other before becoming roommates–from three public universities over the course of their first year in college, surveying them at the start of the year and the end of each semester. We obtained grade reports and course withdrawal data from the university registrars. In this investigation, we use the terms “students” and “roommates” in lieu of “actors” and “partners”, which is the typical language used in investigations of dyadic data. We do this to aid readers who are less familiar with dyadic analyses. Importantly, in this investigation all participants simultaneously hold the role of both “students” and “roommates”. We examine three sets of questions, which we divide into phases in the analyses for greater clarity:First, in Phase 1, we examined what happens to students’ own academic outcomes when they either enter college with heightened depressive symptoms or are assigned a roommate with heightened depressive symptoms. Further, we tested whether any effects were moderated by roommate closeness. To answer these questions, we analyzed whether depressive symptoms at the start of the year predicted 1) the student’s own GPA for the Fall and Spring semesters; 2) their roommate’s GPA for Fall and Spring; 3) student’s own withdrawal from individual classes ; and 4) roommate’s withdrawal from individual classes . 5) We then examined whether each of these potential associations was moderated by a dyadic measure of roommate closeness.Second, in Phase 2, we examined whether GPAs, course withdrawals, and depressive affect are contagious between roommates by computing associations between the two roommates’ scores on these measures at each time point ; and testing whether these relationships are moderated by relationship closeness. If academic outcomes and depressive symptoms are socially contagious, the roommates’ scores should become more correlated over time. If contagion occurs only when roommates are close, the moderation by closeness interaction terms should be significant.Third, in Phase 3, we examined whether students’ depressive symptoms early in the academic year ultimately predicted roommates’ fall and spring academic outcomes. As shown in Based on previous research, we expected that student’s own depressive symptoms would predict their own academic outcomes, with higher depressive symptoms predicting lower GPAs and more course withdrawals. In addition, based on studies of social contagion between unacquainted roommates, we predicted contagion of academic outcomes and depression, and tested whether this contagion is stronger when the roommate relationship is closer. Because no research has examined how students’ depression might affect their roommates’ academic outcomes, these analyses are exploratory. This study allows an unprecedented look into how depression shapes the academic lives of college students and their roommates over the course of the first year in college.M = 2.99 weeks, SD = 1.31). Participants ranged in age from 18 to 29  and 82% reported that their race was Caucasian/white, 11% reported their race as Asian American, 8% reported their race was African American/Black, 6% reported their race was Latino/a, 1% reported their race as American Indian/Alaska Native, 1% reported their race as Middle Eastern, and 2% selected other . These data were collected as part of a larger project for which the target number of dyads was 225. A power analysis for dyadic data , p = .006). Closeness of roommates at T1 was not significantly related to attrition at T2 (Exp(-.18) = .84, 95% CI for Exp(B) , p = .358) or T3 (Exp(-.13) = .88, 95% CI for Exp(B) , p = .514).Attrition in this study was relatively low; of the 490 participants who began the study, 431 (88%) completed the T2 survey, and 429 (87.6%) completed the T3 survey at the end of the spring semester. Students’ depressive symptoms at T1 were not significantly related to attrition at T2 (Exp(.14) = 1.15, 95% CI for Exp(B) , Attrition contributed to missing values for depressive symptoms at T2 (12%) and T3 (12.4%). Additionally, a similar proportion did not report standardized test scores (12.2%). Missingness was low for fall and spring GPAs  and fall and spring course withdrawals . There were no missing data for T1 depressive symptoms or dyadic closeness. Missing data were treated using listwise deletion in Phase 1 and 2 analyses. Because Phase 3 analyses required complete data  in order to accommodate bootstrapping to test indirect effects, we treated missing data using Bayesian imputation.p < .001; spring: b = -.14, t(398.95) = -3.01, 95% CI , p = .003). Students whose T1 depressive symptoms were at 1 SD below the sample mean  had a predicted fall GPA of 3.36 and spring GPA of 3.33, whereas students whose T1 depressive symptoms were at 1 SD above the mean , had a predicted GPA of 3.07 in fall and 3.03 in spring. Roommates’ T1 depressive symptoms were not significantly related to students’ GPAs in either semester  = -1.81, 95% CI , p = .072; spring: b = -.03, t(395.78) = -.67, 95% CI , p = .501). Dyadic closeness did not moderate any of these associations .We tested whether students’ and roommates’ T1 depressive symptoms predicted students’ fall and spring GPAs. In two separate analyses, we regressed students’ fall and spring GPAs on students’ and roommates’ T1 depressive symptoms and students’ standardized test scores. Students’ T1 depressive symptoms predicted their lower GPAs in both semesters  = -3.90, 95% CI , p = .005) and by 46% in the spring semester (Exp(.38) = 1.46, 95% CI for Exp(B) , p = .002). Roommates’ T1 depressive symptoms were not significantly related to the likelihood of students’ course withdrawals in either semester  = .92, 95% CI for Exp(B) , p = .571; spring: Exp(.01) = .94, 95% CI for Exp(B) , p = .936). Dyadic closeness did not moderate any of these associations (ps ≥.286).Next, we tested whether students’ and roommates’ T1 depressive symptoms predicted students’ course withdrawals during the fall and spring semesters. When we regressed students’ fall and spring withdrawals on students’ and roommates’ depressive symptoms at T1 in two separate analyses, students’ T1 depressive symptoms predicted their greater likelihood of withdrawing from at least one course both semesters. Specifically, a one SD increase in students’ T1 depressive symptoms increased the odds of their withdrawing from a class by 60% in the fall semester (Exp(.47) = 1.60, 95% CI for Exp(B) , These findings replicate previous research demonstrating that students’ depressive symptoms are associated with their own lowered GPAs and a greater likelihood of withdrawing from at least one course. Roommates’ depressive symptoms did not predict students’ academic performance. These associations did not depend on levels of closeness between roommates.p < .001). When roommates’ fall GPAs were lower , the predicted value of students’ fall GPAs was 3.09, whereas when roommates’ fall GPAs were higher , the predicted value of students’ fall GPAs was 3.37. In a similar analysis of GPA contagion in the spring semester, roommates’ spring GPA positively predicted students’ spring GPA  = 4.22, 95% CI , p < .001). When roommates’ spring GPAs were lower (a GPA of 2.32), the predicted value of students’ spring GPAs was 3.07, whereas when roommates’ spring GPAs were higher (a GPA of 3.94), the predicted value of students’ spring GPAs was 3.41.We tested the contagion of GPA during the fall and spring semesters. In these analyses, we controlled for both people’s standardized test scores. When we regressed students’ fall GPA on roommates’ fall GPA and students’ and roommates’ standardized test scores, roommates’ fall GPA positively predicted students’ fall GPA  = 3.79, 95% CI , p = .001) and spring  = 3.97, 95% CI , p < .001).Because in Phase 1 analyses, students’ T1 depressive symptom were related to their fall and spring GPAs and roommates’ T1 depressive symptoms were marginally related to students’ fall GPA, we conducted these analyses a second time, including students’ and roommates’ T1 depressive symptoms as additional covariates. Results remained unchanged: roommates’ and students’ GPAs were positively related in fall  = 3.20, 95% CI , p = .028, pseudo ΔR2 = .01, see upper panel of p = .016, pseudo ΔR2 = .02; see lower panel of t(329.83) = 4.08, 95% CI , p < .001; spring: b = .29, t(258.50) = 4.84, 95% CI , p < .001), but were not significantly related when T1 dyadic closeness was one standard deviation below the mean  = 1.76, 95% CI , p = .080; spring: b = .08, t(296.84) = 1.08, 95% CI , p = .282). Johnson-Neyman analyses conducted using the method described by Preacher and colleagues , p = .471).We simplified the analyses by conducting logistic regressions. When roommates withdrew from at least one course in the fall semester, the likelihood that students also withdrew from at least one course that semester was almost three times higher (Exp(1.00) = 2.73, 95% Wald CI for Exp(B) , p = .013) but not in spring (Exp(-.34) = .71, 95% Wald CI for Exp(B) , p = .461). Dyadic closeness did not moderate contagion of withdrawals in the fall  or spring .Because in Phase 1 analyses, students’ T1 depressive symptoms were related to their fall and spring course withdrawals, we conducted these analyses a second time, including students’ T1 depressive symptoms as a covariate.  Results remained unchanged: roommates’ and students’ withdrawal from at least one class were positively related in fall (Exp(1.10) = 3.00, 95% Wald CI for Exp(B) , t(428.83) = .59, 95% CI , p = .553). This association was not moderated by dyadic closeness .We examined the contagion of T1, T2, and T3 depressive symptoms. In three separate models, we regressed students’ depressive symptoms onto roommates’ depressive symptoms within T1, T2, and T3. As would be expected given unacquainted dyads, roommates’ and students’ depressive symptoms were not significantly related at T1  = 2.61, 95% CI , p = .009). This association was moderated by dyadic closeness  = 3.77, 95% CI , p < .001) but not when dyadic closeness at T1 was one standard deviation below the mean  = -.44, 95% CI , p = .661). Johnson-Neyman analyses suggest that contagion of depressive symptoms in the fall occurred when T1 dyadic closeness was at or greater than.13 standard deviation below the mean. That is, contagion of depressive symptoms in the fall was significant when dyads reported mutual closeness of 3.11 and higher at T1.Depressive symptoms were contagious at T2. Roommates’ T2 depressive symptoms predicted students’ T2 depressive symptoms  = 2.48, 95% CI , p = .014). Again, this association was moderated by dyadic closeness  = 3.24, 95% CI , p = .001) but not when T1 dyadic closeness was at one standard deviation below the mean = .14, 95% CI , p = .887). Johnson-Neyman analyses suggest that contagion of depressive symptoms in the spring occurred when T1 dyadic closeness was at or greater than.20 standard deviation below the mean. Thus, contagion of depressive symptoms was significant when dyads reported mutual closeness of 2.58 and higher at T1.Depressive symptoms were also contagious at T3. Roommates’ T3 depressive symptoms predicted students’ greater T3 depressive symptoms , the predicted values of students’ depressive symptoms were 13.66 and 15.41 at the end of the fall and spring semesters, respectively. Notable, these scores are just below the threshold for risk of mild to moderate depression. However, when roommates’ depressive symptoms were higher , the predicted values of students’ depressive symptoms were 16.46 and 18.39 at the end of fall and spring, respectively. These scores are just above the threshold for risk of mild to moderate depression.These findings provide evidence that GPA and depressive symptoms are contagious between roommates across the academic year and that contagion is strongest when roommates feel closer to each other at the beginning of the academic year. The data also provide evidence of contagion for the likelihood of withdrawing from at least one class in the fall semester, but not in the spring and this association does not depend on roommate closeness in either semester.Taken together, the findings thus far suggest that students’ T1 depressive symptoms may have downstream consequences for roommates’ T2 or T3 academic outcomes through contagion of depression. Thus, we examined whether students’ T1 depressive symptoms and the contagion of depressive symptoms between roommates at T2 and T3 predicted roommates’ GPA and course withdrawals in the fall and spring, respectively. Specifically, we tested two path models. The first model focused on contagion and outcomes at T2, testing whether students’ T1 depressive symptoms predicted their own T2 depressive symptoms, which predicted roommates’ T2 depressive symptoms, which in turn predicted roommates’ GPA and course withdrawals in the fall semester. The path coefficients for these models appear in p < .001). The path from students’ T2 depressive symptoms to roommates’ T2 depressive symptoms was moderated by dyadic closeness . Roommates’ T2 depressive symptoms, in turn, predicted their lower fall GPA . The index of conditional mediation was significant , such that the indirect effect was significant for dyads who were higher in closeness  but not for dyads who were lower in closeness . That is, students’ T1 depressive symptoms indirectly predicted roommates’ GPA in the fall semester, but only when closeness was higher.In the model predicting roommates’ fall GPA, students’ T1 depressive symptoms predicted students’ T2 depressive symptoms . The path from students’ T2 depressive symptoms and roommates’ T2 depressive symptoms was moderated by dyadic closeness . Roommates’ T2 depressive symptoms, in turn, predicted their increased fall course withdrawals . The index of conditional mediation was significant , such that the indirect effect was significant for dyads who were higher in closeness  but not for dyads who were lower in closeness . That is, students’ T1 depressive symptoms indirectly predicted roommates’ course withdrawals in the fall semester, but again only when closeness was higher.In the model predicting roommates’ fall withdrawals, students’ T1 depressive symptoms predicted students’ T2 depressive symptoms . The path from students’ T3 depressive symptoms and roommates’ T3 depressive symptoms was moderated by dyadic closeness . Roommates’ T3 depressive symptoms, in turn, predicted their lower spring GPA . The index of conditional mediation was significant ; the indirect effect was negative and significant for dyads who were higher in closeness  but positive and significant for dyads who were lower in closeness . That is, students’ T1 depressive symptoms indirectly predicted roommates’ GPA in the spring. The direction of this indirect association depended on closeness. In closer dyads, students’ higher depressive symptoms indirectly predicted roommates’ lower GPA, but in less close dyads, students’ higher depressive symptoms indirectly predicted roommates’ higher GPA.In the model predicting roommates’ spring GPA, students’ T1 depressive symptoms predicted students’ T3 depressive symptoms . The path from students’ T3 depressive symptoms and roommates’ T2 depressive symptoms was moderated by dyadic closeness . Roommates’ T3 depressive symptoms, in turn, predicted their increased spring withdrawals . The index of conditional mediation was significant , such that the indirect effect was positive and significant for dyads who were higher in closeness  but negative and significant for dyads who were lower in closeness . That is, students’ T1 depressive symptoms indirectly predicted roommates’ higher course withdrawal in the spring. The direction of this indirect association depended on closeness. In closer dyads, students’ higher depressive symptoms indirectly predicted roommates’ higher course withdrawal, but in less close dyads, students’ higher depressive symptoms indirectly predicted roommates’ lower course withdrawal.In the model predicting roommates’ spring withdrawals, students’ T1 depressive symptoms predicted students’ greater T3 depressive symptoms  are women. Although we recruited all incoming first year students at participating universities, women were more likely to respond to recruitment efforts. Because women have higher depression rates, they may be more affected by emotional contagion then men , and areThe current study was conducted across three large public American universities, which may also constrain the generality of our findings. Results might be different at small colleges with smaller classes or greater overlap of peers in multiple classes. Furthermore, at many institutions of higher education, students do not live on campus and may commute from home. For these students, contagion of depressive symptoms and negative effects on academic outcomes may be more likely to occur in familial relationships. In addition, the research was conducted only at American universities. These results may not generalize to universities and roommate relationships outside of the United States. Future research should examine the generalizability of these findings to other types of academic institutions.Finally, we followed student outcomes for one year, but it may be that over the course of multiple years the effects we observed either multiply or fade. We also had a relatively low number of students who withdrew from courses each semester , limiting our power to find an effect. Also, as with all correlational studies, it may be there are unmeasured variables that help to explain the effects. A study examining depression and academics through graduation would shed more light on the early influences of depression and roommate relationships and allow for greater specificity in predictors.The present study provides important information for students and universities as they consider what might happen when students start college–either with heightened depressive symptoms themselves or if they are assigned a roommate with heightened depressive symptoms. The good news is that regardless of depressive symptoms, students can form close friendships with their roommates and succeed academically. However, depression can affect grades and may spread between close roommates. Thus, for students suffering from depressive symptoms it is important to seek help and for universities to have ample help available. This will not only improve one’s own mental health and academic outcomes but may also help their new roommate.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Data(SAV)Click here for additional data file.


The effect of amyloid-β (Aβ) on cognitive impairment in patients with small subcortical infarction remains controversial, although a growing body of evidence shows a substantial overlap between Alzheimer’s disease (AD) and subcortical ischemic vascular dementia, another form of cerebral small vessel disease (cSVD). Therefore, we investigated the relationships between Aβ positivity and the development of post-stroke cognitive impairment (PSCI) in patients with small subcortical infarction.We prospectively recruited 37 patients aged ≥ 50 years, with first-ever small subcortical infarction, who underwent amyloid positron emission tomography, 3 months after stroke at Korea University Guro Hospital. We also enrolled CU participants matched for age and sex with stroke patients for comparison of Aβ positivity. Patients were followed up at 3 and 12 months after the stroke to assess cognitive decline. Logistic and linear mixed-effect regression analyses were performed to identify the effect of Aβ positivity on PSCI development and long-term cognitive trajectories.p = 0.024) was predictive of PSCI development regardless of cSVD burden. Aβ positivity  was also associated with poor cognitive trajectory, assessed by the Clinical Dementia Rating-Sum of Box, for 1 year after stroke.At 3 months after stroke, 12/37 (32.4%) patients developed PSCI, and 11/37 (29.7%) patients had Aβ deposition. Aβ positivity (odds ratio [OR] = 72.2, Our findings highlight that Aβ positivity is an important predictor for PSCI development and cognitive decline over 1 year. Furthermore, our results provide evidence that anti-AD medications may be a strategy for preventing cognitive decline in patients with small subcortical infarctions. A considerable number of post-stroke survivors suffer from post-stroke cognitive impairment (PSCI), which in turn leads to impaired activities of daily living and an increased burden on caregivers, regardless of physical disability –3. AlzheCerebral small vessel disease (cSVD) causes various clinical syndromes, including subcortical ischemic vascular dementia (SIVD) and lacunar syndromes. Different from PSCI, a growing body of evidence shows that AD and SIVD affect one another interactively , and Aβ Therefore, in this study, we investigated the relationship between Aβ deposition and development of PSCI. Additionally, we explored the cognitive trajectory after ischemic stroke based on Aβ positivity. To minimize the effect of stroke lesion size and strategic site, participants were limited to those with small subcortical infarctions. Considering the substantial overlap between AD and SIVD, we hypothesized that Aβ deposition is predictive of the development of PSCI in patients with small subcortical infarctions.Patients, aged ≥ 50 years with first-ever ischemic stroke, who were admitted to Korea University Guro Hospital within 7 days of ischemic stroke were screened for enrollment eligibility from June 1, 2021, to April 1, 2022. At baseline, all patients with ischemic stroke underwent a comprehensive stroke evaluation, including neurological examination using the National Institutes of Health Stroke Scale (NIHSS), functional outcome using the modified Rankin scale, and brain magnetic resonance imaging (MRI).We prospectively enrolled patients with small subcortical infarctions to eliminate the effects of the stroke lesion size and strategic site. We excluded patients with the following conditions: (1) strategic infarcts involving the anterior thalamus and hippocampus; (2) severe aphasia (NIHSS language score > 1), visual impairment, or physical disabilities  due to ischemic stroke; and (3) presence of premorbid cognitive impairment, neurodegenerative diseases, lobar hemorrhage, or psychiatric disorders.Premorbid cognitive impairment was determined using the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) , answereHypertension was defined as a diagnostic history of hypertension or current use of any antihypertensive medication, and diabetes was defined as a diagnostic history of diabetes or current use of any anti-diabetic medication.We also enrolled CU participants matched for age and sex with stroke patients. These participants did not have a history of stroke, neurodegenerative disease, or psychiatric disorders and were composed of spouses of patients who visited the neurology clinic, volunteers who applied for comprehensive dementia evaluation advertised in the paper, and participants who had cognitive complaints. They visited the Memory Clinic in the Department of Neurology at Korea University Guro Hospital and underwent a comprehensive dementia evaluation.All CU participants met the following criteria: (1) no medical history which is likely to affect cognitive function based on Christensen’s health screening criteria , (2) no Hypertension was defined as a diagnostic history of hypertension or current use of any antihypertensive medication, and diabetes was defined as a diagnostic history of diabetes or current use of any anti-diabetic medication.This study was approved by the Institutional Review Board of the Korea University Guro Hospital. Written informed consent was obtained from all participants.All patients with stroke were followed up at 3 and 12 months after ischemic stroke. At the follow-up visit at 3 months, the patients underwent a neuropsychological battery using the Korean version of the Vascular Cognitive Impairment Harmonization Standards neuropsychological battery (K-VCIHS-NP) , Korean CU participants without stroke were also followed up at 12 months after the initial comprehensive evaluation. At the follow-up visit at 12 months, CU participants also underwent the K-MMSE and CDR-SOB.z-scores using the age, sex, and education criteria presented in the K-VCIHS-NP, and the z-scores were used in the analysis.Patients underwent neuropsychological testing using the K-VCIHS-NP . Seven cPSCI was defined according to the modified Peterson criteria and results of the K-VCIHS-NP. In the K-VCIHS-NP, cognitive functions were classified as impaired when objective cognitive impairment was − 1.5 SD on at least two different cognitive domains. The frontal domain consists of four neuropsychological tests. Cognitive impairment in the frontal domain was classified as objective cognitive impairment − 1.5 SD on two or more tests.18F-florbetaben PET using a discovery MI PET/computed tomography (CT) scanner . A 20-min emission PET scan in dynamic mode (comprising 4 × 5 min frames) was performed 90 min after the injection of a mean dose of 296 MBq 18F-florbetaben. Three-dimensional PET images were reconstructed in a 384 × 384 matrix with 0.65 × 0.65 × 2·79 mm voxel size using the ordered-subsets expectation maximization algorithm (iteration = 8 and subset = 34).The patients underwent 18F-florbetaben PET was classified as positive when interpreters scored the visual assessment as 2 or 3 on the brain amyloid-plaque load (BAPL) score [Amyloid PET images were reviewed by three experienced physicians (one neurologist and two nuclear medicine doctors) who were blinded to clinical information and dichotomized as either Aβ positive or negative using visual reads . 18F-floL) score , 12. Spek = 0.89). After the physicians individually rated, we determined the final Aβ positivity based on the majority of the visual reading result.An RCTU score of 1 in each brain region corresponded to a BAPL score of 1, an RCTU score of 2 in any brain region and no score of 3 corresponded to a BAPL score of 2, and an RCTU score of 3 in any of the four brain regions corresponded to a BAPL score of 3. Inter-rater agreement was excellent  images using a 3.0-T MRI scanner  by following imaging parameters: sagittal slice thickness, 0.6 mm; no gap; TR, 4800 ms; TE, 363 ms; flip angle, 90°; and matrix size, 288 × 287 pixels. As described previously , the CliBriefly, deep WMH (DWMH) were classified as D1 (< 10 mm), D2 (10–25 mm), or D3 (≥ 25 mm) based on the longest lesion diameter. Periventricular WMH (PWMH) were classified as P1 (cap and band < 5 mm), P2 (5–10 mm), or P3 (cap or band ≥ 10 mm) based on the maximum length measured perpendicular (cap) and horizontal (band) to the ventricle. The combination of these D and P ratings yielded nine cells and the overall WMH severity  was defined based on the following combinations of D and P ratings: minimal (D1P1 and D1P2), moderate , and severe (D3P3).We also counted the number of microbleeds (MBs), defined as ≤ 10 mm in diameter on 150 axial slices of T2 susceptibility-weighted imaging-MRI by following imaging parameters: sagittal slice thickness, 2.0 mm; no gap; TR, 24 ms; TE, 0 ms; flip angle, 18°; and matrix size, 384 × 383 pixels . Strictlt-tests and chi-squared tests were used. To assess the effect of stroke on longitudinal cognitive changes, we performed a linear mixed-effect regression analysis and included stroke group (stroke vs CU without stroke), time, and stroke group × time as fixed effects, along with age, sex, years of education, hypertension, diabetes, Aβ positivity, and WMH severity. The patients were included as random effects. To identify the association of stroke with cognitive function at 3 months and 12 months, we performed linear regression analyses with the stroke group as a predictor after controlling for with age, sex, years of education, hypertension, diabetes, Aβ positivity, and WMH severity.For comparison between the clinical characteristics of stroke and CU without stroke groups, independent t-tests and chi-squared tests were used to compare the clinical characteristics of the post-stroke normal cognition (PSNC) and PSCI groups. To investigate the association between Aβ deposition and the development of PSCI, we performed a logistic regression analysis with Aβ positivity, hypertension, diabetes, and WMH severity as predictors and PSCI development as an outcome after controlling for age, sex, and years of education. To assess the effect of Aβ positivity on longitudinal cognitive changes, we performed a linear mixed-effect regression analysis and included Aβ positivity, time, and Aβ positivity × time as fixed effects, along with age, sex, years of education, hypertension, diabetes, and WMH severity. The patients were included as random effects. To identify the association of Aβ positivity with cognitive function at 3 months and 12 months, we performed linear regression analyses with Aβ positivity as a predictor after controlling for with age, sex, years of education, hypertension, diabetes, and WMH severity.In stroke groups, independent p-values were two-sided, and the significance level was set at 0.05. All analyses were performed using R version 4.3.0 .All reported p = 0.920), female ratio (p = 1.000), years of education (p = 0.196), and rates of diabetes (p = 1.000) did not differ between the patients with stroke and CU participants without stroke. Patients with stroke had a higher frequency of hypertension  and WMH burdens  than CU participants without stroke. None of the patients with stroke and CU participants had CAA markers including cSS and strictly lobar MBs.Of the 52 patients with stroke prospectively enrolled for the baseline evaluation, 15 missed the cognitive assessment follow-up at 3 months. The mean age (p = 0.238), female ratio (p = 0.228), years of education (p = 0.128), and rates of hypertension (p = 0.168) did not differ between the PSCI and PSNC groups. Patients with PSCI had a higher frequency of diabetes (58.3%) than  those with PSNC .Regarding the development of PSCI in patients with stroke, 3 months after the stroke, 12/37 (32.4%) patients developed PSCI (Table Aβ positivity was higher in the stroke group (29.7%) than in the age- and sex-matched CU without stroke group (16.2%), while the difference was not significant.β = − 2.079, p = 0.004; at 12 months, β = − 2.392, p = 0.002) and CDR-SOB scores , irrespective of Aβ positivity and WMH severity. However, the stroke group was not associated with faster deterioration in CDR-SOB  and K-MMSE score , after controlling for Aβ positivity and WMH severity.Compared with the CU without stroke group, the stroke group was associated with lower K-MMSE  was associated with PSCI development, independently of WMH (Table p = 0.042) and severe WMH  were associated with PSCI development.Among patients with small subcortical infarction, Aβ positivity , while Aβ positivity was not associated with faster decline in K-MMSE score  and CDR-SOB scores , except for K-MMSE score at 3 months .Longitudinal changes in K-MMSE or CDR-SOB scores based on Aβ positivity among patients with small subcortical infarctions are illustrated in Fig. In the present study, we investigated the relationships between Aβ deposition and the development of PSCI in patients with small subcortical infarctions. Our major findings are as follows: First, Aβ positivity was 29.7% in patients with small subcortical infarctions, which was higher than that in CU participants without stroke. Second, Aβ positivity was predictive of PSCI development regardless of cSVD burden. Finally, Aβ positivity was associated with a poor cognitive trajectory 1 year after stroke. Taken together, our findings suggest that Aβ positivity is an important predictor for PSCI development and cognitive decline over the course of 1 year. Furthermore, considering that AD-related cognitive decline is potentially delayed by pharmacological agents, our results provide evidence that anti-AD medications may be a strategy for preventing cognitive decline in patients with small subcortical infarctions.p = 0.002) [Our first major finding was that Aβ positivity was 29.7% in patients with small subcortical infarctions. This frequency was consistent with previous studies, which reported approximately 30% of Aβ positivity in patients with cerebral infarction and SIVD –19. In t= 0.002) . Conside= 0.002) , 20–24, = 0.002) . Alterna= 0.002) , 27. The= 0.002) , 29.We also found that patients with small subcortical infarctions had a higher frequency of hypertension and WMH burdens than CU participants without stroke. Considering the relationship between small subcortical infarction and cSVD, this finding was what we expected. Hypertension is the most important modifiable risk factor for cSVD .Our second major finding was that Aβ positivity was predictive of PSCI development regardless of the cSVD burden, suggesting that coexistent AD pathology may play a vital role in the development of PSCI after stroke. This finding is comparable to previous findings that Aβ positivity is an important factor in PSCI development , 31, 32.Although further studies are required to elucidate the mechanism underlying the relationship between Aβ positivity and PSCI, neuroinflammation may mediate this association. Neuroinflammation is a common trigger for neuronal damage in individuals with Aβ and cerebral infarctions. It is well known that Aβ deposition in the brain starts to accumulate 10 years before the onset of clinical symptoms. After the deposition of Aβ, neuroinflammation may lead to neurodegeneration, a surrogate marker of cognitive decline, even in the non-demented stage . SpecifiWe also found that pre-existing WMH and diabetes were predictive of PSCI development. Although the association between pre-existing WMH and PSCI development was expected, this was again replicated in our groups, a finding suggestive of the importance of pre-existing WMH on cognitive impairment after small subcortical infarctions –39. RegaOur final major finding was that Aβ positivity was associated with faster deterioration in CDR-SOB for 1 year after stroke. In patients with small subcortical infarctions, the relationship between Aβ positivity and long-term cognitive trajectory remains controversial. Our findings suggest that Aβ positivity is associated with delayed-onset PSCI. Recent studies using plasma AD biomarkers reported comparable results , 43. Aβ In the present study, we investigated the association of Aβ deposition on PET with the development of PSCI and long-term prognosis in patients with small subcortical infarction. However, our study had several limitations that need to be addressed. First, the sample size is relatively small. Second, we could not assess long-term cognitive trajectories using a detailed neuropsychological battery. However, this is mitigated to a certain extent by the fact that the CDR-SOB score is a well-validated outcome measure that is widely used in clinical trials of AD and PSCI. Third, the generalizability of the present study to all patients with stroke should be treated with caution because only participants with small subcortical infarctions were enrolled. Nevertheless, our study provides a comprehensive understanding of the relationship between Aβ positivity and cognitive impairment in patients with small subcortical infarctions.In conclusion, Aβ positivity is an important predictor for the development of PSCI. Furthermore, preclinical AD pathology was predictive of a poor cognitive trajectory. Therefore, anti-AD medications may contribute to delayed cognitive decline in patients with small subcortical infarctions, although it is necessary to select the patient carefully with consideration for the MBs count and WMH severity.


In Germany, oral emergency contraception (EC) with the active ingredients levonorgestrel (LNG) and ulipristal acetate (UPA) is available as over-the-counter (OTC) medicine only from community pharmacies (CPs). Because of the window of effect, which is limited to only a few days, CPs have a great responsibility to provide rapid and unimpeded access, while also ensuring “adequate” counseling. The aim was—for the first time in Europe and thus also in Germany for the methodology used in this study—to investigate immediate availability, pricing, and aspects of counseling.®, citing a contraceptive failure one day ago as the reason.Covert mystery calls were conducted in a random sample of CPs stratified by districts in the German capital Berlin. Each of the 263 CPs included was called once at random by one of two trained female student mystery callers. They simulated a product-based scenario for the UPA original ellaOneOf 257 successfully called CPs, UPA preparations were immediately available in 98.4% (253/257) and LNG preparations in 86.8% (184/212) of CPs. Prices for UPA preparations varied from €15.95 to €42.95  €5.91]) and for LNG preparations from €10.60 to €32.49 . Information about the correct different window of effect of UPA and LNG preparations was provided in 69.8% (127/182) of CPs. UPA preparations were recommended in 63.1% (111/176) and LNG preparations in 17.2% (30/174) of CPs. Information was provided on how to take them as soon as possible in 30.8% (44/143) of CPs and on how to use them after vomiting in 46.0% (64/139).Berlin CPs support access through high immediate availability, especially to UPA preparations. However, access is hampered by high absolute price ranges of both UPA and LNG preparations, which could ideally be minimized by a comparison app. It is positive that CPs promote the benefits of UPA preparations by recommending them noticeably more often than LNG preparations. However, there are deficiencies in giving advice, so there is a need to raise awareness among pharmacy staff to ensure “adequate” counseling in advance over the phone.The online version contains supplementary material available at 10.1186/s40545-023-00565-w. The UPA original ellaOne® (n = 224) had a median price of €35.07 [IQR €2.07] with a price range from €25.50 to €42.95  and UPA generics (n = 50) had a median price of €24.90 [IQR €1.38] with a price range from €15.95 to €32.00 . For LNG preparations (n = 164), the median price was €22.00 [IQR €5.76] with a price range from €10.60 to €32.49 . Thus, the median price for UPA preparations was 59.1% higher than the median price for LNG preparations. Prices for Berlin districts are reported in Additional file The median price for UPA preparations (ellaOne® and generics) n = 41 was €369.8% (127/182), and thus the majority of CPs, provided information about the correct different window of effect of UPA and LNG preparations. UPA preparations were recommended far more frequently  than LNG preparations . In 30.8% (44/143), information was given about taking the drug as soon as possible and in 46.0% (64/139) about retaking after vomiting.p = 0.002, V = 0.309), with an effect size corresponding to a “medium” effect according to Cohen [. In post hoc analyses, a call duration between 2:01 and 3:00 min (Benjamini–Hochberg adjusted p = 0.044), between 3:01 and 4:00 min (Benjamini–Hochberg adjusted p = 0.010), or above 4:00 min (Benjamini–Hochberg adjusted p = 0.010) compared to 1:01 to 2:00 min were significantly associated with increased “giving advice on the correct different window of effect”. Similarly, the “call duration” and the “giving advice on retaking after vomiting”  showed a significant association, which also had a “medium” effect size [p = 0.012) or above 4:00 min (Benjamini–Hochberg adjusted p = 0.012) compared to 1:01 to 2:00 min were significantly associated with increased “giving advice on retaking after vomiting”. For “giving advice on retaking after vomiting”, there was also a significant association with “districts of the CPs” , also with a “medium” effect [2 [p = 0.013, V = 0.210), although the effect size was considered “small” [There were no significant associations between the availability and prices of UPA and LNG preparations (items 1–8 of the assessment form) and the possible influencing factors. Table to Cohen . In postect size . Post ho” effect . In post “small” .Table 6AIn line with a German self-reported study , the CPsIn this MC study, the median price for UPA preparations was more than half higher than the median price for LNG preparations. Although comparative studies from Germany are lacking, it is known at least from international SPM studies that in the USA  and in Within UPA and LNG preparations, enormous price ranges were present in this MC study, analogous to two German SPM studies on acute diarrhea. Thus, in a medium-sized city with 84 on-site visits in 21 CPs each, these two determined prices, which also included both originals and generics, from €2.36 to €8.49 Δ €6.13, 260%)  and from0%  and f® versus UPA generics). However, higher-priced medicines can represent a financial burden for patients above a certain price threshold and thus a barrier to access to oral EC, which is urgently needed at a given time.A certain price difference between UPA and LNG preparations does not seem surprising at first, as UPA preparations are superior to LNG preparations with regard to efficacy and window of effect , 7. HoweIn particular, to minimize the high absolute price ranges for oral EC and their potential consequences, price transparency should be increased —despite UPA preparations were recommended noticeably more frequently than LNG preparations in this MC study in line with a Turkish SPM study  and a GeGiving advice was overall—as in international MC studies with comparable counseling items in the USA , 56, 58,Furthermore, in this MC study, certain factors had an influence on some assessment items. For example, giving advice on the correct different window of effect and the retaking after vomiting of the oral EC were significantly associated with the call duration. This could also be expected, since longer duration of conversation may also result in more giving advice. Unlike other indications , the SPMTo the authors’ knowledge, this is the first SPM study in Europe, and thus also in Germany, to examine practices of CPs on oral EC, including immediate availability, pricing as well as preparation recommendations and giving advice as aspects of counseling. The SPM in the form of mystery calls, internationally referred to as the “gold standard” , was appThe study only covered the city of Berlin in the northeast of Germany, which means that the results are not transferable to other cities, especially in geographically different regions, and also not to rural regions. In addition, although the minimum sample size was slightly undercut with a subsequent continuous decrease in the sample sizes of the items of the assessment form, the results can still show a realistic picture of the practices of the Berlin CPs with regard to oral EC.®, an assessment of LNG preparations divided into original and generic products was not possible.The respective mystery calls were conducted by two different MCs, which may have led to an averaging of the MCs' personal characteristics (voice) and thus to the depiction of even more realistic conversational situations. In addition, only objective items and dichotomous scales were used in order to avoid a subjective assessment and thus the MCs' leeway in assessment, which is typical for SPM studies . Nevertheless, the results of the mystery calls conducted may differ from those obtained during on-site visits. Furthermore, due to the execution of one mystery call per CP, the answers refer to only one member of the pharmacy staff. Due to the demand of the MCs explicitly for ellaOneThe inclusion in the assessment form of the possible questions for information gathering of the pharmacy staff provided for in the scenario could have enabled a fully comprehensive assessment of the counseling and also a better assessment of the respective preparation recommendation. However, due to the large number of items to be remembered, there would then have been a risk of bias in the results due to possible missing or faulty memories  of the MCs.No specific measures were taken to find out whether mystery callers were identified by the pharmacy staff. This would have required the willingness to cooperate of the CPs studied , which iBerlin CPs support access through high immediate availability, especially to UPA preparations. However, access is hampered by high absolute price ranges of both UPA and LNG preparations, which could ideally be minimized by a comparison app. It is positive that CPs promote the benefits of UPA preparations by recommending them noticeably more often than LNG preparations. However, there are deficiencies in giving advice, so there is a need to raise awareness among pharmacy staff to ensure "adequate" counseling in advance over the phone.Additional file 1. Example for a written, pharmacy-specific performance feedback using graphically edited benchmarking.Additional file 2. Price of UPA medications (n = 241) by district, median price (minimum price - maximum price). Price of ellaOne® (n = 224) by district, median price (minimum price - maximum price). Price of UPA generics (n = 50) by district, median price (minimum price - maximum price). Price of LNG medications (n = 160) by district, median price (minimum price - maximum price).