An analysis of the Lowest Total Fertility Rate in Hong Kong SAR

Total Fertility Rate (TFR) in Hong Kong has dropped significantly over the past 30 years, from 2.48 births per woman in 1976 to 0.966 in 2005, which is one of the lowest in the world. It is mainly caused by the change of marital distribution which has contributed to about 56% of the decline in the total fertility rate for the period 1976-2001. Delay of marriage and reduction in the marital fertility rate have also been shown to be two major causes for the low TFR. A new measure, called a weighted total marital fertility rate (WTMFR), is introduced such that change of age at marriage and the fertility within marriage can be factored in explaining the decline of the fertility rate. The delay of marriage has contributed to about 52% of the reduction of WTMFR whereas the reduction of the fertility within marriage has accounted for the other 48%. Apparently, the proportion of women remaining single has been stabilized and leveled off recently. However, the preference of having smaller family size has become a norm rather than an exception. It is very unlikely to see a rebound of fertility among the Hong Kong women in the near future if there is no increase in marriages or births outside wedlock. Encouraging more births among married women so as to increase fertility is expected to have limited impact.Age at first marriage, decomposition, Hong Kong, Total fertility rate, Weighted total marital fertility rate

Evolving Tumor Characteristics and Smart Nanodrugs for Tumor Immunotherapy

Wenshe Sun,1– 3,* Shaowei Xie,4,* Shi Feng Liu,1,* Xiaokun Hu,1 Dongming Xing1,2 1The Affiliated Hospital of Qingdao University, Qingdao, 266071, People’s Republic of China; 2Qingdao Cancer Institute, Qingdao University, Qingdao, 266071, People’s Republic of China; 3Medical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People’s Republic of China; 4Department of Ultrasound, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaokun Hu; Dongming Xing, The Affiliated Hospital of Qingdao University, Qingdao, 266071, People’s Republic of China, Email [email protected]; [email protected]: Typical physiological characteristics of tumors, such as weak acidity, low oxygen content, and upregulation of certain enzymes in the tumor microenvironment (TME), provide survival advantages when exposed to targeted attacks by drugs and responsive nanomedicines. Consequently, cancer treatment has significantly progressed in recent years. However, the evolution and adaptation of tumor characteristics still pose many challenges for current treatment methods. Therefore, efficient and precise cancer treatments require an understanding of the heterogeneity degree of various factors in cancer cells during tumor evolution to exploit the typical TME characteristics and manage the mutation process. The highly heterogeneous tumor and infiltrating stromal cells, immune cells, and extracellular components collectively form a unique TME, which plays a crucial role in tumor malignancy, including proliferation, invasion, metastasis, and immune escape. Therefore, the development of new treatment methods that can adapt to the evolutionary characteristics of tumors has become an intense focus in current cancer treatment research. This paper explores the latest understanding of cancer evolution, focusing on how tumors use new antigens to shape their “new faces”; how immune system cells, such as cytotoxic T cells, regulatory T cells, macrophages, and natural killer cells, help tumors become “invisible”, that is, immune escape; whether the diverse cancer-associated fibroblasts provide support and coordination for tumors; and whether it is possible to attack tumors in reverse. This paper discusses the limitations of targeted therapy driven by tumor evolution factors and explores future strategies and the potential of intelligent nanomedicines, including the systematic coordination of tumor evolution factors and adaptive methods, to meet this therapeutic challenge.Keywords: smart nanomedicine, tumor evolution, immune cells, fibroblast

Abrogating cholesterol esterification suppresses growth and metastasis of pancreatic cancer

Cancer cells are known to execute reprogramed metabolism of glucose, amino acids and lipids. Here, we report a significant role of cholesterol metabolism in cancer metastasis. By using label-free Raman spectromicroscopy, we found an aberrant accumulation of cholesteryl ester in human pancreatic cancer specimens and cell lines, mediated by acyl-CoA cholesterol acyltransferase-1 (ACAT-1) enzyme. Expression of ACAT-1 showed a correlation with poor patient survival. Abrogation of cholesterol esterification, either by an ACAT-1 inhibitor or by shRNA knockdown, significantly suppressed tumor growth and metastasis in an orthotopic mouse model of pancreatic cancer. Mechanically, ACAT-1 inhibition increased intracellular free cholesterol level, which was associated with elevated endoplasmic reticulum stress and caused apoptosis. Collectively, our results demonstrate a new strategy for treating metastatic pancreatic cancer by inhibiting cholesterol esterification

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Parenchymal involvement on CT pulmonary angiography in SARS-CoV-2 Alpha variant infection and correlation of COVID-19 CT severity score with clinical disease severity and short-term prognosis in a UK cohort

AIM: To determine if there is a difference in radiological, biochemical, or clinical severity between patients infected with Alpha-variant SARS-CoV-2 compared with those infected with pre-existing strains, and to determine if the computed tomography (CT) severity score (CTSS) for COVID-19 pneumonitis correlates with clinical severity and can prognosticate outcomes. MATERIALS AND METHODS: Blinded CTSS scoring was applied to 137 hospital patients who had undergone both CT pulmonary angiography (CTPA) and whole-genome sequencing of SARS-CoV-2 within 14 days of CTPA between 1/12/20–5/1/21. RESULTS: There was no evidence of a difference in imaging severity on CTPA, viral load, clinical parameters of severity, or outcomes between Alpha and preceding variants. CTSS on CTPA strongly correlates with clinical and biochemical severity at the time of CTPA, and with patient outcomes. Classifying CTSS into a binary value of “high” and “low”, with a cut-off score of 14, patients with a high score have a significantly increased risk of deterioration, as defined by subsequent admission to critical care or death (multivariate hazard ratio [HR] 2.76, p<0.001), and hospital length of stay (17.4 versus 7.9 days, p<0.0001). CONCLUSION: There was no evidence of a difference in radiological severity of Alpha variant infection compared with pre-existing strains. High CTSS applied to CTPA is associated with increased risk of COVID-19 severity and poorer clinical outcomes and may be of use particularly in settings where CT is not performed for diagnosis of COVID-19 but rather is used following clinical deterioration

Proceedings of the Second Annual Conference of the MidSouth Computational Biology and Bioinformatics Society

The MCBIOS 2004 conference brought together regional researchers and students in biology, computer science and bioinformatics on October 7th-9th 2004 to present their latest work. This editorial describes the conference itself and introduces the twelve peer-reviewed manuscripts accepted for publication in the Proceedings of the MCBIOS 2004 Conference. These manuscripts included new methods for analysis of high-throughput gene expression experiments, EST clustering, analysis of mass spectrometry data and genomic analysi

New mutations at the imprinted Gnas cluster show gene dosage effects of Gsα in postnatal growth and implicate XLαs in bone and fat metabolism, but not in suckling

The imprinted Gnas cluster is involved in obesity, energy metabolism, feeding behavior, and viability. Relative contribution of paternally expressed proteins XLαs, XLN1, and ALEX or a double dose of maternally expressed Gsα to phenotype has not been established. In this study, we have generated two new mutants (Ex1A-T-CON and Ex1A-T) at the Gnas cluster. Paternal inheritance of Ex1A-T-CON leads to loss of imprinting of Gsα, resulting in preweaning growth retardation followed by catch-up growth. Paternal inheritance of Ex1A-T leads to loss of imprinting of Gsα and loss of expression of XLαs and XLN1. These mice have severe preweaning growth retardation and incomplete catch-up growth. They are fully viable probably because suckling is unimpaired, unlike mutants in which the expression of all the known paternally expressed Gnasxl proteins (XLαs, XLN1 and ALEX) is compromised. We suggest that loss of ALEX is most likely responsible for the suckling defects previously observed. In adults, paternal inheritance of Ex1A-T results in an increased metabolic rate and reductions in fat mass, leptin, and bone mineral density attributable to loss of XLαs. This is, to our knowledge, the first report describing a role for XLαs in bone metabolism. We propose that XLαs is involved in the regulation of bone and adipocyte metabolism

Fe3O4–Au and Fe2O3–Au Hybrid Nanorods: Layer-by-Layer Assembly Synthesis and Their Magnetic and Optical Properties

A layer-by-layer technique has been developed to synthesize FeOOH–Au hybrid nanorods that can be transformed into Fe2O3–Au and Fe3O4–Au hybrid nanorods via controllable annealing process. The homogenous deposition of Au nanoparticles onto the surface of FeOOH nanorods can be attributed to the strong electrostatic attraction between metal ions and polyelectrolyte-modified FeOOH nanorods. The annealing atmosphere controls the phase transformation from FeOOH–Au to Fe3O4–Au and α-Fe2O3–Au. Moreover, the magnetic and optical properties of as-synthesized Fe2O3–Au and Fe3O4–Au hybrid nanorods have been investigated

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure