*Article* **Biopolymer Material from Human Spongiosa for Regenerative Medicine Application**

**Ilya L. Tsiklin , Evgeniy I. Pugachev, Alexandr V. Kolsanov, Elena V. Timchenko \*, Violetta V. Boltovskaya, Pavel E. Timchenko and Larisa T. Volova**

> Biotechnology Center "Biotech", Samara State Medical University, 443079 Samara, Russia<EMAIL_ADDRESS>(I.L.T<EMAIL_ADDRESS>(E.I.P<EMAIL_ADDRESS>(A.V.K<EMAIL_ADDRESS>(V.V.B<EMAIL_ADDRESS>(P.E.T<EMAIL_ADDRESS>(L.T.V.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +79-277-111-387

**Abstract:** Natural biopolymers demonstrate significant bone and connective tissue-engineering application efficiency. However, the quality of the biopolymer directly depends on microstructure and biochemical properties. This study aims to investigate the biocompatibility and microstructural properties of demineralized human spongiosa Lyoplast® (Samara, Russian Federation). The graft's microstructural and biochemical properties were analyzed by scanning electron microscopy (SEM), micro-computed tomography, Raman spectroscopy, and proteomic analysis. Furthermore, the cell adhesion property of the graft was evaluated using cell cultures and fluorescence microscopy. Microstructural analysis revealed the hierarchical porous structure of the graft with complete removal of the cellular debris and bone marrow components. Moreover, the proteomic analysis confirmed the preservation of collagen and extracellular proteins, stimulating and inhibiting cell adhesion, proliferation, and differentiation. We revealed the adhesion of chondroblast cell cultures in vitro without any evidence of cytotoxicity. According to the study results, demineralized human spongiosa Lyoplast® can be effectively used as the bioactive scaffold for articular hyaline cartilage tissue engineering.

**Keywords:** biopolymers; demineralized human spongiosa; scanning electron microscopy; micro-computed tomography; Raman spectroscopy; proteomic analysis; chondroblasts; tissue engineering; scaffold; fluorophores


4.2.3. Non-Energy Input Effect

Figure 7 shows that the aggregated non-energy input effect of 24 sectors contributed to fossil energy growth. Note that the non-energy input effects of electricity, metallurgy, and the chemical industry weakened their energy savings related to the energy input level effect. The non-energy input effects of the chemical industry and metallurgy generated noticeable fossil energy growth at 65 and 34 Mtce, respectively, although that of electricity was insignificant at 7 Mtce (Figure 7). Thus, the −441 Mtce energy input level effect of electricity was offset by 68 Mtce, as a result of the energy growth from the energy composition effect and the non-energy input effect, resulting in a −373 Mtce of a combined effect from the three factors (i.e., the technology effect). Similarly, the −286 Mtce energy input level effect of metallurgy was offset by 34 Mtce, leading to a −252 Mtce technology effect. The −237 Mtce energy input level effect of the chemical industry was offset by 94 Mtce, generating a −143 Mtce technology effect overall.

In addition to the above, the non-energy input effect of construction triggered the most significant energy growth, leading the fossil energy to increase by 123 Mtce (Figure 7). The other consequential sectors that contributed to energy growth were coal mining and electronic equipment (Figure 7). From Δ**G** in Equation (16), we found that the energy growth induced by the non-energy input effect of construction and metallurgy mainly came from the increased metallurgy products' input. Similarly, the growing chemical industry products input and electronic equipment input were mainly responsible for the energy growth of the chemical industry and the electronic equipment related to the non-energy input effect, respectively. Particularly, the principal input factor that was responsible for the fossil energy growth from the non-energy input effect of coal mining varied province by province, but mainly included growth in the chemical industry products, metallurgy products, electrical equipment, and general and specialist machinery input in the sector of coal mining.

**Figure 7.** Non-energy input effect of each sector.

Figure 8 shows the most influential disaggregated contributors to energy-use related to the non-energy input effect. The energy growth generated from the non-energy input effect of coal mining in Shanxi was the highest, mainly owning to the growing metallurgy products input (Figure 8). The other contributors in the bottom ten were related to construction, metallurgy, the chemical industry, and electronic equipment, not surprisingly (Figure 8).

**Figure 8.** Top ten and bottom ten contributors for energy saving related to non-energy input effect. JL: Jilin; GD: Guangdong; SX: Shanxi; ZJ: Zhejiang; IM: Inner Mongolia; SD: Shandong; HB: Hebei; LN: Liaoning; CQ: Chongqing; JX: Jiangxi; JS: Jiangsu.


2.2.3. Other Perspectives

Dogan and Turkekul [14] analyze the relationship between financial development and carbon emissions in the USA from 1960 to 2010 with the ARDL approach and error correction-based Granger causality test, and conclude that although the financial development could affect the output, it has no effect on carbon emissions in the long-term. Similarly, based on the panel vector autoregressive (PVAR) model and the data of 24 countries in the MENA region from 1980 to 2015, Charfeddine and Kahia [27] find that financial development only slightly influences carbon emissions.

Paramati et al. [28] investigate the relationship between stock markets and carbon emissions in 23 developed and 20 emerging market countries from 1992 to 2011 with the Durbin–Hausman test and the common correlated effects (CCE) approach, and find that the influence of the stock market on carbon emissions differs between developed countries and emerging market countries. More specifically, the stock market indicators significantly negatively affect carbon emissions in developed countries but positively affects emerging market countries.